diff --git a/docs/examples/Sklearn_Gaussian_Regression.ipynb b/docs/examples/Sklearn_Gaussian_Regression.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scikit Learn Walkthorough\n",
+    "\n",
+    "This notebook is a walkthorough of the scikit learn API for XGBoostLSS. It attempts to show how a typical sklearn workflow can be used with XGBoostLSS while still allowing a user to access the additional functionality of XGBoostLSS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/StatMixedML/XGBoostLSS/blob/master/docs/examples/Gaussian_Regression.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports\n",
+    "\n",
+    "First, we import the necessary functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-18T06:24:10.418630300Z",
+     "start_time": "2023-05-18T06:24:10.403008900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pylab as plt\n",
+    "\n",
+    "from xgboostlss.datasets.data_loader import (\n",
+    "    load_simulated_gaussian_data, generate_simulated_gaussian_data\n",
+    ")\n",
+    "\n",
+    "from xgboostlss.sklearn import XGBLSSRegressor\n",
+    "\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import make_scorer\n",
+    "\n",
+    "from scipy.stats.distributions import norm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data\n",
+    "\n",
+    "Let's define some helper functions and take a look at the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def neg_log_likelihood(y_true, y_pred, scipy_dist=norm, param_list=[\"loc\", \"scale\"]):\n",
+    "    \"\"\"\n",
+    "    Negative Log Likelihood (NLL) scorer for sklearn.\n",
+    "\n",
+    "    Uses mean instead of sum to compare train and test sets. For non-gaussian\n",
+    "    distributions, the param_list should be carefully updated to match those\n",
+    "    found in scipy.stats.distributions and ordered as the distributions module.\n",
+    "    \"\"\"\n",
+    "    dist_param_kwargs = dict(zip(param_list, y_pred.T))\n",
+    "    return -np.mean(scipy_dist.logpdf(y_true, **dist_param_kwargs))\n",
+    "\n",
+    "\n",
+    "def train_test_scorer_df(\n",
+    "    y_train,\n",
+    "    y_pred_train,\n",
+    "    y_test,\n",
+    "    y_pred_test,\n",
+    "    test_run_name,\n",
+    "    scorers=[neg_log_likelihood],\n",
+    "    score_df=None,\n",
+    "    scorer_kwargs=None,\n",
+    "):\n",
+    "    \"\"\"Compute scores for train and test sets.\"\"\"\n",
+    "    score_list = []\n",
+    "    if scorer_kwargs is None:\n",
+    "        scorer_kwargs = {}\n",
+    "\n",
+    "    for scorer in scorers:\n",
+    "        scorer_name = scorer.__name__\n",
+    "        train_score = scorer(y_train, y_pred_train, **scorer_kwargs)\n",
+    "        test_score = scorer(y_test, y_pred_test, **scorer_kwargs)\n",
+    "\n",
+    "        score_list.append(\n",
+    "            pd.DataFrame(\n",
+    "                {\n",
+    "                    \"scorer\": scorer_name,\n",
+    "                    \"train_score\": train_score,\n",
+    "                    \"test_score\": test_score,\n",
+    "                },\n",
+    "                index=[test_run_name],\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    if score_df is None:\n",
+    "        score_df = pd.concat(score_list, axis=0)\n",
+    "    else:\n",
+    "        score_df = pd.concat([score_df] + score_list, axis=0)\n",
+    "    return score_df.drop_duplicates(keep='last')\n",
+    "\n",
+    "\n",
+    "nll_scorer = make_scorer(neg_log_likelihood, greater_is_better=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-18T06:12:13.097935100Z",
+     "start_time": "2023-05-18T06:12:03.538184Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x294072800>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train, test = load_simulated_gaussian_data()\n",
+    "\n",
+    "X_train, y_train = train.filter(regex=\"x\"), train[\"y\"]\n",
+    "X_test, y_test = test.filter(regex=\"x\"), test[\"y\"]\n",
+    "\n",
+    "X_train_sorted = X_train.sort_values(\"x_true\")\n",
+    "X_test_sorted = X_test.sort_values(\"x_true\")\n",
+    "\n",
+    "y_train_scale = generate_simulated_gaussian_data(X_train_sorted)\n",
+    "y_test_scale = generate_simulated_gaussian_data(X_test_sorted)\n",
+    "\n",
+    "y_train_true = pd.DataFrame(data={\"loc\":10, \"scale\": generate_simulated_gaussian_data(X_train)})\n",
+    "y_test_true = pd.DataFrame(data={\"loc\":10, \"scale\": generate_simulated_gaussian_data(X_test)})\n",
+    "\n",
+    "X_train_plot_df = (\n",
+    "    X_train_sorted\n",
+    "    .assign(\n",
+    "        y_train_scale_ub=10 + y_train_scale,\n",
+    "        y_train_scale_lb=10 - y_train_scale,\n",
+    "    )\n",
+    "    .set_index(\"x_true\")\n",
+    ")\n",
+    "\n",
+    "X_test_plot_df = (\n",
+    "    X_test_sorted\n",
+    "    .assign(\n",
+    "        y_test_scale_ub=10 + y_test_scale,\n",
+    "        y_test_scale_lb=10 - y_test_scale,\n",
+    "    )\n",
+    "    .set_index(\"x_true\")\n",
+    ")\n",
+    "\n",
+    "ax = train.plot.scatter(x=\"x_true\", y=\"y\", label=\"train\", color=\"C0\", s=2)\n",
+    "ax.set_title(\"Training Data with True Scales\")\n",
+    "X_train_plot_df.filter(like=\"scale\").assign(loc=10).plot(\n",
+    "    ax=ax, linestyle=\"--\", color=[\"k\", \"k\", \"r\"]\n",
+    ")\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a benchmark score from the known distribution and compare with our test set.\n",
+    "\n",
+    "Here the loc parameter is plotted, but will be excluded in future plots as it is constant and of little interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scorer</th>\n",
+       "      <th>train_score</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Original Params</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.07076</td>\n",
+       "      <td>2.082057</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             scorer  train_score  test_score\n",
+       "Original Params  neg_log_likelihood      2.07076    2.082057"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nll_score_df = train_test_scorer_df(\n",
+    "    y_train.values, y_train_true.values, y_test.values, y_test_true.values, \"Original Params\"\n",
+    ")\n",
+    "\n",
+    "ax = test.plot.scatter(x=\"x_true\", y=\"y\", label=\"train\", color=\"C0\", s=2)\n",
+    "X_test_plot_df.filter(like=\"scale\").assign(loc=10).plot(ax=ax, linestyle=\"--\", color=[\"k\", \"k\", \"r\"])\n",
+    "\n",
+    "ax.set_title(\"Test Data with True Scales\")\n",
+    "\n",
+    "nll_score_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Parameters and Defaults\n",
+    "\n",
+    "For XGBoostLSS models, the most important parameter is the distribution. To make getting started with the scikit-learn API as easy as possible, the `XGBLSSRegressor` defaults to a using the `Gaussian` distribution. This is somewhat similar to [GaussianProcessRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html) in scikit-learn, which default to using `Constant` and an `RBF` kernels in the simplest case.\n",
+    "\n",
+    "It should be noted that the distribution choice dictates loss/objective function. Both these terms will be used interchangeably as XGBoost uses the term `objective` and scikit-learn uses the term `loss`. Where possible, `XGBLSSRegressor` will use the scikit-learn terminology.\n",
+    "\n",
+    "Other than the `distribution` and lack of the `loss` parameters, all standard [Sklearn XGBoost](https://xgboost.readthedocs.io/en/stable/python/python_api.html#module-xgboost.sklearn) parameters should be usable.\n",
+    "\n",
+    "Let's have a play with some of the more familiar parameters. Remember, if unspecified, this defaults to a Gaussian distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-18T06:12:13.097935100Z",
+     "start_time": "2023-05-18T06:12:04.423429800Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scorer</th>\n",
+       "      <th>train_score</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Original Params</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.070760</td>\n",
+       "      <td>2.082057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hand Tuned</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.111516</td>\n",
+       "      <td>2.143074</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             scorer  train_score  test_score\n",
+       "Original Params  neg_log_likelihood     2.070760    2.082057\n",
+       "Hand Tuned       neg_log_likelihood     2.111516    2.143074"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgblss = XGBLSSRegressor(\n",
+    "    max_depth=2,\n",
+    "    learning_rate=0.05,\n",
+    "    n_estimators=50,\n",
+    "    subsample=0.7,\n",
+    "    colsample_bytree=0.7,\n",
+    ")\n",
+    "\n",
+    "xgblss.fit(X_train, y_train)\n",
+    "y_pred_train = xgblss.predict(X_train)\n",
+    "y_pred_test = xgblss.predict(X_test)\n",
+    "\n",
+    "test_pred_df = (\n",
+    "    X_test\n",
+    "    .assign(loc=y_pred_test[:, 0], scale=y_pred_test[:, 1])\n",
+    "    .assign(y=y_test)\n",
+    "    .sort_values(\"x_true\")\n",
+    "    .assign(\n",
+    "        scale_ub=lambda df: df[\"loc\"] + df[\"scale\"],\n",
+    "        scale_lb=lambda df: df[\"loc\"] - df[\"scale\"],\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "ax = test_pred_df.plot.scatter(x=\"x_true\", y=\"y\", s=2)\n",
+    "test_pred_df.plot(x=\"x_true\", y='loc', ax=ax, c='r')\n",
+    "test_pred_df.plot(\n",
+    "    x=\"x_true\",\n",
+    "    y=['scale_ub', 'scale_lb'],\n",
+    "    ax=ax,\n",
+    "    style='--',\n",
+    "    color='C1',\n",
+    ")\n",
+    "\n",
+    "X_test_plot_df.filter(like=\"scale\").plot(ax=ax, linestyle=\"--\", c=\"k\")\n",
+    "\n",
+    "(\n",
+    "    neg_log_likelihood(y_train, y_train_true.values),\n",
+    "    neg_log_likelihood(y_train, y_pred_train),\n",
+    "    neg_log_likelihood(y_test, y_test_true.values),\n",
+    "    neg_log_likelihood(y_test, y_pred_test),\n",
+    ")\n",
+    "\n",
+    "nll_score_df = train_test_scorer_df(y_train, y_pred_train, y_test, y_pred_test, \"Hand Tuned\", score_df=nll_score_df)\n",
+    "nll_score_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More Sklearn Machinery\n",
+    "\n",
+    "Amazing! We have our first scikit learn XGBoostLSS model. Now we can go one to tune the hyperparameters using standard sklearn machinery. Here is a rough and ready example using grid search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
+       "             estimator=XGBLSSRegressor(base_score=0, booster=None,\n",
+       "                                       callbacks=None, colsample_bylevel=None,\n",
+       "                                       colsample_bynode=None,\n",
+       "                                       colsample_bytree=0.7, device=None,\n",
+       "                                       dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                                       early_stopping_rounds=None,\n",
+       "                                       enable_categorical=False,\n",
+       "                                       eval_metric=None, feature_types=None,\n",
+       "                                       gamma=None, grow_policy=None,\n",
+       "                                       im...\n",
+       "                                       max_delta_step=None, max_depth=2,\n",
+       "                                       max_leaves=None, min_child_weight=None,\n",
+       "                                       missing=nan, monotone_constraints=None,\n",
+       "                                       multi_strategy=None, n_estimators=50,\n",
+       "                                       n_jobs=None, num_parallel_tree=None, ...),\n",
+       "             param_grid={&#x27;learning_rate&#x27;: array([0.1       , 0.02154435, 0.00464159, 0.001     ]),\n",
+       "                         &#x27;max_depth&#x27;: array([2, 6]),\n",
+       "                         &#x27;n_estimators&#x27;: array([ 10,  37, 136, 501])},\n",
+       "             scoring=make_scorer(neg_log_likelihood, greater_is_better=False))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
+       "             estimator=XGBLSSRegressor(base_score=0, booster=None,\n",
+       "                                       callbacks=None, colsample_bylevel=None,\n",
+       "                                       colsample_bynode=None,\n",
+       "                                       colsample_bytree=0.7, device=None,\n",
+       "                                       dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                                       early_stopping_rounds=None,\n",
+       "                                       enable_categorical=False,\n",
+       "                                       eval_metric=None, feature_types=None,\n",
+       "                                       gamma=None, grow_policy=None,\n",
+       "                                       im...\n",
+       "                                       max_delta_step=None, max_depth=2,\n",
+       "                                       max_leaves=None, min_child_weight=None,\n",
+       "                                       missing=nan, monotone_constraints=None,\n",
+       "                                       multi_strategy=None, n_estimators=50,\n",
+       "                                       n_jobs=None, num_parallel_tree=None, ...),\n",
+       "             param_grid={&#x27;learning_rate&#x27;: array([0.1       , 0.02154435, 0.00464159, 0.001     ]),\n",
+       "                         &#x27;max_depth&#x27;: array([2, 6]),\n",
+       "                         &#x27;n_estimators&#x27;: array([ 10,  37, 136, 501])},\n",
+       "             scoring=make_scorer(neg_log_likelihood, greater_is_better=False))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBLSSRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBLSSRegressor(base_score=0, booster=None, callbacks=None,\n",
+       "                colsample_bylevel=None, colsample_bynode=None,\n",
+       "                colsample_bytree=0.7, device=None,\n",
+       "                dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                early_stopping_rounds=None, enable_categorical=False,\n",
+       "                eval_metric=None, feature_types=None, gamma=None,\n",
+       "                grow_policy=None, importance_type=None,\n",
+       "                interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
+       "                max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "                max_delta_step=None, max_depth=2, max_leaves=None,\n",
+       "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "                multi_strategy=None, n_estimators=50, n_jobs=None,\n",
+       "                num_parallel_tree=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBLSSRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBLSSRegressor(base_score=0, booster=None, callbacks=None,\n",
+       "                colsample_bylevel=None, colsample_bynode=None,\n",
+       "                colsample_bytree=0.7, device=None,\n",
+       "                dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                early_stopping_rounds=None, enable_categorical=False,\n",
+       "                eval_metric=None, feature_types=None, gamma=None,\n",
+       "                grow_policy=None, importance_type=None,\n",
+       "                interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
+       "                max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "                max_delta_step=None, max_depth=2, max_leaves=None,\n",
+       "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "                multi_strategy=None, n_estimators=50, n_jobs=None,\n",
+       "                num_parallel_tree=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=3,\n",
+       "             estimator=XGBLSSRegressor(base_score=0, booster=None,\n",
+       "                                       callbacks=None, colsample_bylevel=None,\n",
+       "                                       colsample_bynode=None,\n",
+       "                                       colsample_bytree=0.7, device=None,\n",
+       "                                       dist=<xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830>,\n",
+       "                                       early_stopping_rounds=None,\n",
+       "                                       enable_categorical=False,\n",
+       "                                       eval_metric=None, feature_types=None,\n",
+       "                                       gamma=None, grow_policy=None,\n",
+       "                                       im...\n",
+       "                                       max_delta_step=None, max_depth=2,\n",
+       "                                       max_leaves=None, min_child_weight=None,\n",
+       "                                       missing=nan, monotone_constraints=None,\n",
+       "                                       multi_strategy=None, n_estimators=50,\n",
+       "                                       n_jobs=None, num_parallel_tree=None, ...),\n",
+       "             param_grid={'learning_rate': array([0.1       , 0.02154435, 0.00464159, 0.001     ]),\n",
+       "                         'max_depth': array([2, 6]),\n",
+       "                         'n_estimators': array([ 10,  37, 136, 501])},\n",
+       "             scoring=make_scorer(neg_log_likelihood, greater_is_better=False))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    \"learning_rate\": np.logspace(-1, -3, 4),\n",
+    "    \"max_depth\": np.array(range(2, 8, 4)),\n",
+    "    \"n_estimators\": np.logspace(1, 2.7, 4).round(0).astype(int),\n",
+    "}\n",
+    "\n",
+    "gscv = GridSearchCV(\n",
+    "    xgblss,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=3,\n",
+    "    scoring=nll_scorer,\n",
+    ")\n",
+    "\n",
+    "gscv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scorer</th>\n",
+       "      <th>train_score</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Original Params</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.070760</td>\n",
+       "      <td>2.082057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hand Tuned</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.111516</td>\n",
+       "      <td>2.143074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Grid Search</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.033965</td>\n",
+       "      <td>2.099166</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             scorer  train_score  test_score\n",
+       "Original Params  neg_log_likelihood     2.070760    2.082057\n",
+       "Hand Tuned       neg_log_likelihood     2.111516    2.143074\n",
+       "Grid Search      neg_log_likelihood     2.033965    2.099166"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgblss_best_est = gscv.best_estimator_\n",
+    "y_pred_train = xgblss_best_est.predict(X_train)\n",
+    "y_pred_test = xgblss_best_est.predict(X_test)\n",
+    "\n",
+    "test_pred_df = (\n",
+    "    X_test\n",
+    "    .assign(loc=y_pred_test[:, 0], scale=y_pred_test[:, 1])\n",
+    "    .assign(y=y_test)\n",
+    "    .sort_values(\"x_true\")\n",
+    "    .assign(\n",
+    "        scale_ub=lambda df: df[\"loc\"] + df[\"scale\"],\n",
+    "        scale_lb=lambda df: df[\"loc\"] - df[\"scale\"],\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "ax = test_pred_df.plot.scatter(x=\"x_true\", y=\"y\")\n",
+    "test_pred_df.plot(x=\"x_true\", y='loc', ax=ax, color='r')\n",
+    "\n",
+    "test_pred_df.plot(\n",
+    "    x=\"x_true\",\n",
+    "    y=['scale_ub', 'scale_lb'],\n",
+    "    ax=ax,\n",
+    "    style='--',\n",
+    "    color='C1',\n",
+    ")\n",
+    "\n",
+    "X_test_plot_df.filter(like=\"scale\").plot(ax=ax, linestyle=\"--\", c=\"k\")\n",
+    "\n",
+    "nll_score_df = train_test_scorer_df(y_train, y_pred_train, y_test, y_pred_test, \"Grid Search\", score_df=nll_score_df)\n",
+    "nll_score_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optuna\n",
+    "\n",
+    "The grid searchhas given us something of an improvement, however, it is rather clunky and looks to be overfitting. XGBoostLSS comes with hyperparameter optimisation built on top of [optuna](https://optuna.org/). This is a much more efficient way of searching the hyperparameter space. We can however, use the sklearn API with optuna to stay in the sklearn ecosystem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/3_/hpdpjt6j0f731cvcrx_9h18w0000gr/T/ipykernel_43974/39289325.py:19: ExperimentalWarning: OptunaSearchCV is experimental (supported from v0.17.0). The interface can change in the future.\n",
+      "  oscv = OptunaSearchCV(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/optuna/integration/sklearn.py:355: RuntimeWarning: Mean of empty slice\n",
+      "  trial.set_user_attr(\"mean_{}\".format(name), np.nanmean(array))\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1879: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "[W 2024-01-22 23:09:24,827] Trial 0 failed with parameters: {'learning_rate': 0.3547860807226683, 'n_estimators': 293, 'max_depth': 9, 'gamma': 3.802342384153437e-05, 'subsample': 0.8502229540463625, 'colsample_bytree': 0.9478540526828161, 'min_child_weight': 7.806479028421288e-08} because of the following error: The value nan is not acceptable.\n",
+      "[W 2024-01-22 23:09:24,827] Trial 0 failed with value nan.\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/optuna/integration/sklearn.py:355: RuntimeWarning: Mean of empty slice\n",
+      "  trial.set_user_attr(\"mean_{}\".format(name), np.nanmean(array))\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1879: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
+      "  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "[W 2024-01-22 23:09:36,497] Trial 4 failed with parameters: {'learning_rate': 0.9201862571256144, 'n_estimators': 165, 'max_depth': 7, 'gamma': 3.845143913862048e-07, 'subsample': 0.8970943490689197, 'colsample_bytree': 0.564261722285122, 'min_child_weight': 2.9590265563675375e-06} because of the following error: The value nan is not acceptable.\n",
+      "[W 2024-01-22 23:09:36,497] Trial 4 failed with value nan.\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n",
+      "/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 810, in _score\n",
+      "    scores = scorer(estimator, X_test, y_test)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 266, in __call__\n",
+      "    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 353, in _score\n",
+      "    y_pred = method_caller(estimator, \"predict\", X)\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 86, in _cached_call\n",
+      "    result, _ = _get_response_values(\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/sklearn/utils/_response.py\", line 218, in _get_response_values\n",
+      "    y_pred, pos_label = estimator.predict(X), None\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/sklearn.py\", line 236, in predict\n",
+      "    y_pred = self._BoosterLSS.predict(\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/model.py\", line 500, in predict\n",
+      "    predt_df = self.dist.predict_dist(booster=self.booster,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 394, in predict_dist\n",
+      "    pred_samples_df = self.draw_samples(predt_params=dist_params_predt,\n",
+      "  File \"/Users/Josh.Dunn/XGBoostLSS/xgboostlss/distributions/distribution_utils.py\", line 324, in draw_samples\n",
+      "    dist_samples = dist_pred.sample((n_samples,)).squeeze().detach().numpy().T\n",
+      "  File \"/Users/Josh.Dunn/mambaforge/envs/xgblss/lib/python3.10/site-packages/torch/distributions/normal.py\", line 70, in sample\n",
+      "    return torch.normal(self.loc.expand(shape), self.scale.expand(shape))\n",
+      "RuntimeError: normal expects all elements of std >= 0.0\n",
+      "\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OptunaSearchCV(cv=3,\n",
+       "               estimator=XGBLSSRegressor(base_score=0, booster=None,\n",
+       "                                         callbacks=None, colsample_bylevel=None,\n",
+       "                                         colsample_bynode=None,\n",
+       "                                         colsample_bytree=0.7, device=None,\n",
+       "                                         dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                                         early_stopping_rounds=None,\n",
+       "                                         enable_categorical=False,\n",
+       "                                         eval_metric=None, feature_types=None,\n",
+       "                                         gamma=None, grow_policy=None,...\n",
+       "                                    &#x27;max_depth&#x27;: IntDistribution(high=10, log=False, low=1, step=1),\n",
+       "                                    &#x27;min_child_weight&#x27;: FloatDistribution(high=500.0, log=True, low=1e-08, step=None),\n",
+       "                                    &#x27;n_estimators&#x27;: IntDistribution(high=500, log=False, low=10, step=1),\n",
+       "                                    &#x27;subsample&#x27;: FloatDistribution(high=1.0, log=False, low=0.2, step=None)},\n",
+       "               scoring=make_scorer(neg_log_likelihood, greater_is_better=False),\n",
+       "               timeout=60, verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OptunaSearchCV</label><div class=\"sk-toggleable__content\"><pre>OptunaSearchCV(cv=3,\n",
+       "               estimator=XGBLSSRegressor(base_score=0, booster=None,\n",
+       "                                         callbacks=None, colsample_bylevel=None,\n",
+       "                                         colsample_bynode=None,\n",
+       "                                         colsample_bytree=0.7, device=None,\n",
+       "                                         dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                                         early_stopping_rounds=None,\n",
+       "                                         enable_categorical=False,\n",
+       "                                         eval_metric=None, feature_types=None,\n",
+       "                                         gamma=None, grow_policy=None,...\n",
+       "                                    &#x27;max_depth&#x27;: IntDistribution(high=10, log=False, low=1, step=1),\n",
+       "                                    &#x27;min_child_weight&#x27;: FloatDistribution(high=500.0, log=True, low=1e-08, step=None),\n",
+       "                                    &#x27;n_estimators&#x27;: IntDistribution(high=500, log=False, low=10, step=1),\n",
+       "                                    &#x27;subsample&#x27;: FloatDistribution(high=1.0, log=False, low=0.2, step=None)},\n",
+       "               scoring=make_scorer(neg_log_likelihood, greater_is_better=False),\n",
+       "               timeout=60, verbose=-1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBLSSRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBLSSRegressor(base_score=0, booster=None, callbacks=None,\n",
+       "                colsample_bylevel=None, colsample_bynode=None,\n",
+       "                colsample_bytree=0.7, device=None,\n",
+       "                dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                early_stopping_rounds=None, enable_categorical=False,\n",
+       "                eval_metric=None, feature_types=None, gamma=None,\n",
+       "                grow_policy=None, importance_type=None,\n",
+       "                interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
+       "                max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "                max_delta_step=None, max_depth=2, max_leaves=None,\n",
+       "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "                multi_strategy=None, n_estimators=50, n_jobs=None,\n",
+       "                num_parallel_tree=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBLSSRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBLSSRegressor(base_score=0, booster=None, callbacks=None,\n",
+       "                colsample_bylevel=None, colsample_bynode=None,\n",
+       "                colsample_bytree=0.7, device=None,\n",
+       "                dist=&lt;xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830&gt;,\n",
+       "                early_stopping_rounds=None, enable_categorical=False,\n",
+       "                eval_metric=None, feature_types=None, gamma=None,\n",
+       "                grow_policy=None, importance_type=None,\n",
+       "                interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
+       "                max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "                max_delta_step=None, max_depth=2, max_leaves=None,\n",
+       "                min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "                multi_strategy=None, n_estimators=50, n_jobs=None,\n",
+       "                num_parallel_tree=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "OptunaSearchCV(cv=3,\n",
+       "               estimator=XGBLSSRegressor(base_score=0, booster=None,\n",
+       "                                         callbacks=None, colsample_bylevel=None,\n",
+       "                                         colsample_bynode=None,\n",
+       "                                         colsample_bytree=0.7, device=None,\n",
+       "                                         dist=<xgboostlss.distributions.Gaussian.Gaussian object at 0x2943c6830>,\n",
+       "                                         early_stopping_rounds=None,\n",
+       "                                         enable_categorical=False,\n",
+       "                                         eval_metric=None, feature_types=None,\n",
+       "                                         gamma=None, grow_policy=None,...\n",
+       "                                    'max_depth': IntDistribution(high=10, log=False, low=1, step=1),\n",
+       "                                    'min_child_weight': FloatDistribution(high=500.0, log=True, low=1e-08, step=None),\n",
+       "                                    'n_estimators': IntDistribution(high=500, log=False, low=10, step=1),\n",
+       "                                    'subsample': FloatDistribution(high=1.0, log=False, low=0.2, step=None)},\n",
+       "               scoring=make_scorer(neg_log_likelihood, greater_is_better=False),\n",
+       "               timeout=60, verbose=-1)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from optuna import logging\n",
+    "from optuna.distributions import FloatDistribution, IntDistribution\n",
+    "from optuna.integration import OptunaSearchCV\n",
+    "\n",
+    "\n",
+    "logging.set_verbosity(logging.WARNING)\n",
+    "\n",
+    "\n",
+    "param_dict = {\n",
+    "    \"learning_rate\": FloatDistribution(low=1e-5, high=1, log=True),\n",
+    "    \"n_estimators\": IntDistribution(low=10, high=500),\n",
+    "    \"max_depth\": IntDistribution(low=1, high=10),\n",
+    "    \"gamma\": FloatDistribution(low=1e-8, high=40, log=True),\n",
+    "    \"subsample\": FloatDistribution(low=0.2, high=1.0, log=False),\n",
+    "    \"colsample_bytree\": FloatDistribution(low=0.2, high=1.0, log=False),\n",
+    "    \"min_child_weight\": FloatDistribution(low=1e-8, high=500, log=True),\n",
+    "}\n",
+    "\n",
+    "oscv = OptunaSearchCV(\n",
+    "    xgblss,\n",
+    "    param_dict,\n",
+    "    timeout=60,\n",
+    "    n_trials=100,\n",
+    "    scoring=nll_scorer,\n",
+    "    cv=3,\n",
+    "    verbose=-1,\n",
+    ")\n",
+    "\n",
+    "oscv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scorer</th>\n",
+       "      <th>train_score</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Original Params</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.070760</td>\n",
+       "      <td>2.082057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hand Tuned</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.111516</td>\n",
+       "      <td>2.143074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Grid Search</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.033965</td>\n",
+       "      <td>2.099166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Optuna</th>\n",
+       "      <td>neg_log_likelihood</td>\n",
+       "      <td>2.066795</td>\n",
+       "      <td>2.095923</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             scorer  train_score  test_score\n",
+       "Original Params  neg_log_likelihood     2.070760    2.082057\n",
+       "Hand Tuned       neg_log_likelihood     2.111516    2.143074\n",
+       "Grid Search      neg_log_likelihood     2.033965    2.099166\n",
+       "Optuna           neg_log_likelihood     2.066795    2.095923"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xgblss_best_est = oscv.best_estimator_\n",
+    "y_pred_train = xgblss_best_est.predict(X_train)\n",
+    "y_pred_test = xgblss_best_est.predict(X_test)\n",
+    "\n",
+    "test_pred_df = (\n",
+    "    X_test\n",
+    "    .assign(loc=y_pred_test[:, 0], scale=y_pred_test[:, 1])\n",
+    "    .assign(y=y_test)\n",
+    "    .sort_values(\"x_true\")\n",
+    "    .assign(\n",
+    "        scale_ub=lambda df: df[\"loc\"] + df[\"scale\"],\n",
+    "        scale_lb=lambda df: df[\"loc\"] - df[\"scale\"],\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "ax = test_pred_df.plot.scatter(x=\"x_true\", y=\"y\")\n",
+    "test_pred_df.plot(x=\"x_true\", y='loc', ax=ax, color='red')\n",
+    "test_pred_df.plot(\n",
+    "    x=\"x_true\",\n",
+    "    y=['scale_ub', 'scale_lb'],\n",
+    "    ax=ax,\n",
+    "    style=['--', '--'],\n",
+    "    color=\"C1\",\n",
+    ")\n",
+    "\n",
+    "X_test_plot_df.filter(like=\"scale\").plot(ax=ax, linestyle=\"--\", c=\"k\")\n",
+    "\n",
+    "nll_score_df = train_test_scorer_df(y_train, y_pred_train, y_test, y_pred_test, \"Optuna\", score_df=nll_score_df)\n",
+    "nll_score_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prediction\n",
+    "\n",
+    "Similar to a XGBoost model, we now predict from the trained model. Different options are available:\n",
+    "\n",
+    "- `samples`: draws `n_samples` from the predicted distribution.\n",
+    "- `quantiles`: calculates quantiles from the predicted distribution.\n",
+    "- `parameters`: returns predicted distributional parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-18T06:22:06.942614600Z",
+     "start_time": "2023-05-18T06:22:06.612277900Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_samples = 10\n",
+    "X_sample = np.hstack(\n",
+    "    [np.array([0.2, 0.4, 0.6, 0.8]).reshape(-1, 1), X_test.iloc[:4, 1:].values]\n",
+    ")\n",
+    "\n",
+    "pred_samples = xgblss_best_est.predict(\n",
+    "    X_sample,\n",
+    "    pred_type=\"samples\",\n",
+    "    n_samples=n_samples,\n",
+    "    validate_features=False,\n",
+    ")\n",
+    "\n",
+    "samples_plot_df = (\n",
+    "    pd.DataFrame(pred_samples, index=X_sample[:, 0])\n",
+    "    .rename_axis(index=\"x_true\", columns=\"sample_n\")\n",
+    "    .unstack()\n",
+    "    .to_frame(\"pred_samples\")\n",
+    ")\n",
+    "\n",
+    "ax = test_pred_df.plot.scatter(x=\"x_true\", y=\"y\")\n",
+    "\n",
+    "for c, df in zip([\"C2\", \"C1\", \"C4\", \"C3\"], samples_plot_df.groupby(\"x_true\")):\n",
+    "    df[1].reset_index().plot.scatter(x=\"x_true\", y=\"pred_samples\", color=c, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot of Actual vs. Predicted Quantiles\n",
+    "\n",
+    "In the following, we plot the predicted quantiles (solid) and compare them to the actual quantiles (dashed)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>scorer</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Optuna Actual 0.05</th>\n",
+       "      <td>mean_pinball_loss</td>\n",
+       "      <td>0.464580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Optuna Predicted 0.05</th>\n",
+       "      <td>mean_pinball_loss</td>\n",
+       "      <td>0.447573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Optuna Actual 0.95</th>\n",
+       "      <td>mean_pinball_loss</td>\n",
+       "      <td>0.459131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Optuna Predicted 0.95</th>\n",
+       "      <td>mean_pinball_loss</td>\n",
+       "      <td>0.438153</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  scorer  test_score\n",
+       "Optuna Actual 0.05     mean_pinball_loss    0.464580\n",
+       "Optuna Predicted 0.05  mean_pinball_loss    0.447573\n",
+       "Optuna Actual 0.95     mean_pinball_loss    0.459131\n",
+       "Optuna Predicted 0.95  mean_pinball_loss    0.438153"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(123)\n",
+    "\n",
+    "quantiles = [0.05, 0.95]\n",
+    "\n",
+    "# Predict Quantiles\n",
+    "pred_quantiles = xgblss_best_est.predict(\n",
+    "    X_test,\n",
+    "    pred_type=\"quantiles\",\n",
+    "    quantiles=quantiles,\n",
+    ")\n",
+    "\n",
+    "pred_quantiles_df = pd.DataFrame(\n",
+    "    pred_quantiles,\n",
+    "    columns=[f\"pred_q_{q}\" for q in quantiles],\n",
+    "    index=X_test.x_true,\n",
+    ")\n",
+    "\n",
+    "# Actual Quantiles\n",
+    "actual_quantiles = np.array([\n",
+    "    norm.ppf(q, loc=10, scale=generate_simulated_gaussian_data(X_test))\n",
+    "    for q in quantiles\n",
+    "])\n",
+    "\n",
+    "actual_quantiles_df = pd.DataFrame(\n",
+    "    actual_quantiles.T,\n",
+    "    columns=[f\"actual_q_{q}\" for q in quantiles],\n",
+    "    index=X_test.x_true,\n",
+    ")\n",
+    "\n",
+    "ax = test_pred_df.plot.scatter(x=\"x_true\", y=\"y\")\n",
+    "pred_quantiles_df.sort_index().plot(ax=ax, color=[\"C1\", \"C2\"], linestyle=\"--\")\n",
+    "actual_quantiles_df.sort_index().plot(ax=ax, color=\"k\", linestyle=\"--\")\n",
+    "\n",
+    "from sklearn.metrics import mean_pinball_loss\n",
+    "\n",
+    "pinball_loss_df = pd.DataFrame()\n",
+    "\n",
+    "for q in quantiles:\n",
+    "    pinball_loss_df = train_test_scorer_df(\n",
+    "        test_pred_df.y,\n",
+    "        pred_quantiles_df[f'pred_q_{q}'],\n",
+    "        test_pred_df.y,\n",
+    "        actual_quantiles_df[f'actual_q_{q}'],\n",
+    "        f\"Optuna Actual {q}\",\n",
+    "        score_df=pinball_loss_df,\n",
+    "        scorers=[mean_pinball_loss],\n",
+    "        scorer_kwargs={\"alpha\": q}\n",
+    "    )\n",
+    "\n",
+    "    pinball_loss_df = train_test_scorer_df(\n",
+    "        test_pred_df.y,\n",
+    "        pred_quantiles_df[f'pred_q_{q}'],\n",
+    "        test_pred_df.y,\n",
+    "        pred_quantiles_df[f'pred_q_{q}'],\n",
+    "        f\"Optuna Predicted {q}\",\n",
+    "        score_df=pinball_loss_df,\n",
+    "        scorers=[mean_pinball_loss],\n",
+    "        scorer_kwargs={\"alpha\": q}\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "pinball_loss_df.drop(columns=[\"train_score\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SHAP Interpretability\n",
+    "\n",
+    "To get a deeper understanding of the data generating process, XGBoostLSS also provides attribute importance and partial dependence plots using the Shapley-Value approach."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-18T06:22:07.616856700Z",
+     "start_time": "2023-05-18T06:22:07.020722700Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          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+      "text/plain": [
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[23:10:21] WARNING: /Users/runner/work/xgboost/xgboost/src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\n"
+     ]
+    },
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+      "text/plain": [
+       "<Figure size 750x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Partial Dependence Plot of how x_true acts on variance\n",
+    "xgblss_best_est._BoosterLSS.plot(\n",
+    "    X_test,\n",
+    "    parameter=\"scale\",\n",
+    "    feature=\"x_true\",\n",
+    "    plot_type=\"Partial_Dependence\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-05-18T06:22:07.960311200Z",
+     "start_time": "2023-05-18T06:22:07.616856700Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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o.componentDidMount&&(t.flags|=4194308),r=!1)}else{o=t.stateNode,Li(e,t),u=t.memoizedProps,s=t.type===t.elementType?u:gi(t.type,u),o.props=s,f=t.pendingProps,p=o.context,l=\"object\"==typeof(l=n.contextType)&&null!==l?Ei(l):za(t,l=La(n)?Pa:Ma.current);var d=n.getDerivedStateFromProps;(c=\"function\"==typeof d||\"function\"==typeof o.getSnapshotBeforeUpdate)||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==f||p!==l)&&Vi(t,o,r,l),Pi=!1,p=t.memoizedState,o.state=p,Ri(t,r,o,a);var h=t.memoizedState;u!==f||p!==h||Na.current||Pi?(\"function\"==typeof d&&(Ii(t,n,d,r),h=t.memoizedState),(s=Pi||Bi(t,n,s,r,p,h,l)||!1)?(c||\"function\"!=typeof o.UNSAFE_componentWillUpdate&&\"function\"!=typeof o.componentWillUpdate||(\"function\"==typeof o.componentWillUpdate&&o.componentWillUpdate(r,h,l),\"function\"==typeof o.UNSAFE_componentWillUpdate&&o.UNSAFE_componentWillUpdate(r,h,l)),\"function\"==typeof o.componentDidUpdate&&(t.flags|=4),\"function\"==typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=l,r=s):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),r=!1)}return Nu(e,t,n,r,i,a)}function Nu(e,t,n,r,a,i){Cu(e,t);var o=0!=(128&t.flags);if(!r&&!o)return a&&Ra(t,n,!1),Hu(e,t,i);r=t.stateNode,bu.current=t;var u=o&&\"function\"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=Ki(t,e.child,null,i),t.child=Ki(t,null,u,i)):wu(e,t,u,i),t.memoizedState=r.state,a&&Ra(t,n,!0),t.child}function Pu(e){var t=e.stateNode;t.pendingContext?Aa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Aa(0,t.context,!1),ro(e,t.containerInfo)}function zu(e,t,n,r,a){return di(),hi(a),t.flags|=256,wu(e,t,n,r),t.child}var Lu,Ou,Au,Fu,Du={dehydrated:null,treeContext:null,retryLane:0};function Ru(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,a=t.pendingProps,o=uo.current,u=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),Ca(uo,1&o),null===e)return si(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:\"$!\"===e.data?t.lanes=8:t.lanes=1073741824,null):(l=a.children,e=a.fallback,u?(a=t.mode,u=t.child,l={mode:\"hidden\",children:l},0==(1&a)&&null!==u?(u.childLanes=0,u.pendingProps=l):u=Rs(l,a,0,null),e=Ds(e,a,n,null),u.return=t,e.return=t,u.sibling=e,t.child=u,t.child.memoizedState=Ru(n),t.memoizedState=Du,e):Uu(t,l));if(null!==(o=e.memoizedState)&&null!==(r=o.dehydrated))return function(e,t,n,r,a,o,u){if(n)return 256&t.flags?(t.flags&=-257,Iu(e,t,u,r=fu(Error(i(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(o=r.fallback,a=t.mode,r=Rs({mode:\"visible\",children:r.children},a,0,null),(o=Ds(o,a,u,null)).flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,0!=(1&t.mode)&&Ki(t,e.child,null,u),t.child.memoizedState=Ru(u),t.memoizedState=Du,o);if(0==(1&t.mode))return Iu(e,t,u,null);if(\"$!\"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,Iu(e,t,u,r=fu(o=Error(i(419)),r,void 0))}if(l=0!=(u&e.childLanes),_u||l){if(null!==(r=Pl)){switch(u&-u){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 536870912:a=268435456;break;default:a=0}0!==(a=0!=(a&(r.suspendedLanes|u))?0:a)&&a!==o.retryLane&&(o.retryLane=a,Ni(e,a),rs(r,e,a,-1))}return gs(),Iu(e,t,u,r=fu(Error(i(421))))}return\"$?\"===a.data?(t.flags|=128,t.child=e.child,t=Ms.bind(null,e),a._reactRetry=t,null):(e=o.treeContext,ri=sa(a.nextSibling),ni=t,ai=!0,ii=null,null!==e&&(Qa[Ya++]=Ka,Qa[Ya++]=Za,Qa[Ya++]=Ga,Ka=e.id,Za=e.overflow,Ga=t),(t=Uu(t,r.children)).flags|=4096,t)}(e,t,l,a,r,o,n);if(u){u=a.fallback,l=t.mode,r=(o=e.child).sibling;var s={mode:\"hidden\",children:a.children};return 0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Wu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(wu(e,t,r.children,n),0!=(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&$u(e,n,t);else if(19===e.tag)$u(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Ca(uo,r),0==(1&t.mode))t.memoizedState=null;else switch(a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===lo(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Bu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===lo(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Bu(t,!0,n,null,i);break;case\"together\":Bu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Vu(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Hu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Rl|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=As(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=As(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function qu(e,t){if(!ai)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Qu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=14680064&a.subtreeFlags,r|=14680064&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yu(e,t,n){var r=t.pendingProps;switch(ti(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Qu(t),null;case 1:case 17:return La(t.type)&&Oa(),Qu(t),null;case 3:return r=t.stateNode,ao(),Ea(Na),Ea(Ma),co(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),null!==e&&null!==e.child||(fi(t)?t.flags|=4:null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==ii&&(us(ii),ii=null))),Ou(e,t),Qu(t),null;case 5:oo(t);var a=no(to.current);if(n=t.type,null!==e&&null!=t.stateNode)Au(e,t,n,r,a),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(i(166));return Qu(t),null}if(e=no(Ji.current),fi(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[pa]=t,r[da]=o,e=0!=(1&t.mode),n){case\"dialog\":Ur(\"cancel\",r),Ur(\"close\",r);break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",r);break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],r);break;case\"source\":Ur(\"error\",r);break;case\"img\":case\"image\":case\"link\":Ur(\"error\",r),Ur(\"load\",r);break;case\"details\":Ur(\"toggle\",r);break;case\"input\":K(r,o),Ur(\"invalid\",r);break;case\"select\":r._wrapperState={wasMultiple:!!o.multiple},Ur(\"invalid\",r);break;case\"textarea\":ae(r,o),Ur(\"invalid\",r)}for(var l in me(n,o),a=null,o)if(o.hasOwnProperty(l)){var s=o[l];\"children\"===l?\"string\"==typeof s?r.textContent!==s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",s]):\"number\"==typeof s&&r.textContent!==\"\"+s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",\"\"+s]):u.hasOwnProperty(l)&&null!=s&&\"onScroll\"===l&&Ur(\"scroll\",r)}switch(n){case\"input\":q(r),J(r,o,!0);break;case\"textarea\":q(r),oe(r);break;case\"select\":case\"option\":break;default:\"function\"==typeof o.onClick&&(r.onclick=Jr)}r=a,t.updateQueue=r,null!==r&&(t.flags|=4)}else{l=9===a.nodeType?a:a.ownerDocument,\"http://www.w3.org/1999/xhtml\"===e&&(e=ue(n)),\"http://www.w3.org/1999/xhtml\"===e?\"script\"===n?((e=l.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild)):\"string\"==typeof r.is?e=l.createElement(n,{is:r.is}):(e=l.createElement(n),\"select\"===n&&(l=e,r.multiple?l.multiple=!0:r.size&&(l.size=r.size))):e=l.createElementNS(e,n),e[pa]=t,e[da]=r,Lu(e,t,!1,!1),t.stateNode=e;e:{switch(l=be(n,r),n){case\"dialog\":Ur(\"cancel\",e),Ur(\"close\",e),a=r;break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",e),a=r;break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],e);a=r;break;case\"source\":Ur(\"error\",e),a=r;break;case\"img\":case\"image\":case\"link\":Ur(\"error\",e),Ur(\"load\",e),a=r;break;case\"details\":Ur(\"toggle\",e),a=r;break;case\"input\":K(e,r),a=G(e,r),Ur(\"invalid\",e);break;case\"option\":default:a=r;break;case\"select\":e._wrapperState={wasMultiple:!!r.multiple},a=R({},r,{value:void 0}),Ur(\"invalid\",e);break;case\"textarea\":ae(e,r),a=re(e,r),Ur(\"invalid\",e)}for(o in me(n,a),s=a)if(s.hasOwnProperty(o)){var c=s[o];\"style\"===o?ge(e,c):\"dangerouslySetInnerHTML\"===o?null!=(c=c?c.__html:void 0)&&fe(e,c):\"children\"===o?\"string\"==typeof c?(\"textarea\"!==n||\"\"!==c)&&pe(e,c):\"number\"==typeof c&&pe(e,\"\"+c):\"suppressContentEditableWarning\"!==o&&\"suppressHydrationWarning\"!==o&&\"autoFocus\"!==o&&(u.hasOwnProperty(o)?null!=c&&\"onScroll\"===o&&Ur(\"scroll\",e):null!=c&&b(e,o,c,l))}switch(n){case\"input\":q(e),J(e,r,!1);break;case\"textarea\":q(e),oe(e);break;case\"option\":null!=r.value&&e.setAttribute(\"value\",\"\"+V(r.value));break;case\"select\":e.multiple=!!r.multiple,null!=(o=r.value)?ne(e,!!r.multiple,o,!1):null!=r.defaultValue&&ne(e,!!r.multiple,r.defaultValue,!0);break;default:\"function\"==typeof a.onClick&&(e.onclick=Jr)}switch(n){case\"button\":case\"input\":case\"select\":case\"textarea\":r=!!r.autoFocus;break e;case\"img\":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return Qu(t),null;case 6:if(e&&null!=t.stateNode)Fu(e,t,e.memoizedProps,r);else{if(\"string\"!=typeof r&&null===t.stateNode)throw Error(i(166));if(n=no(to.current),no(Ji.current),fi(t)){if(r=t.stateNode,n=t.memoizedProps,r[pa]=t,(o=r.nodeValue!==n)&&null!==(e=ni))switch(e.tag){case 3:Xr(r.nodeValue,n,0!=(1&e.mode));break;case 5:!0!==e.memoizedProps.suppressHydrationWarning&&Xr(r.nodeValue,n,0!=(1&e.mode))}o&&(t.flags|=4)}else(r=(9===n.nodeType?n:n.ownerDocument).createTextNode(r))[pa]=t,t.stateNode=r}return Qu(t),null;case 13:if(Ea(uo),r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(ai&&null!==ri&&0!=(1&t.mode)&&0==(128&t.flags))pi(),di(),t.flags|=98560,o=!1;else if(o=fi(t),null!==r&&null!==r.dehydrated){if(null===e){if(!o)throw Error(i(318));if(!(o=null!==(o=t.memoizedState)?o.dehydrated:null))throw Error(i(317));o[pa]=t}else di(),0==(128&t.flags)&&(t.memoizedState=null),t.flags|=4;Qu(t),o=!1}else null!==ii&&(us(ii),ii=null),o=!0;if(!o)return 65536&t.flags?t:null}return 0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return 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+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[23:10:21] WARNING: /Users/runner/work/xgboost/xgboost/src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 800x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Feature Importance of scale parameter\n",
+    "xgblss._BoosterLSS.plot(\n",
+    "    X_test,\n",
+    "    parameter=\"scale\",\n",
+    "    plot_type=\"Feature_Importance\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tests/test_sklearn/__init__.py b/tests/test_sklearn/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/test_sklearn/test_sklearn.py b/tests/test_sklearn/test_sklearn.py
new file mode 100644
index 00000000..f7dae909
--- /dev/null
+++ b/tests/test_sklearn/test_sklearn.py
@@ -0,0 +1,157 @@
+"""Test scikit learn API for XGBoostLSS."""
+
+import numpy as np
+import pandas as pd
+
+from xgboost import Booster
+
+from xgboostlss.sklearn import XGBLSSRegressor
+from xgboostlss.model import XGBoostLSS
+
+from xgboostlss.distributions.Gaussian import Gaussian
+from xgboostlss.distributions.Mixture import Mixture
+from xgboostlss.distributions.SplineFlow import SplineFlow
+from xgboostlss.distributions.Expectile import Expectile
+from xgboostlss.datasets.data_loader import load_simulated_gaussian_data
+import pytest
+from pytest import approx
+
+
+@pytest.fixture
+def univariate_data():
+    train, test = load_simulated_gaussian_data()
+    X_train, y_train = train.filter(regex="x"), train["y"].values
+    X_test, y_test = test.filter(regex="x"), test["y"].values
+
+    return X_train, y_train, X_test, y_test
+
+
+@pytest.fixture
+def univariate_xgblss():
+    params = {
+        "learning_rate": 0.10015347345470738,
+        "max_depth": 8,
+        "gamma": 24.75078796889987,
+        "subsample": 0.6161756203438147,
+        "colsample_bytree": 0.851057889242629,
+        "min_child_weight": 147.09687376037445,
+        "booster": "gbtree",
+        "n_estimators": 98,
+    }
+    return XGBLSSRegressor(Gaussian(), **params)
+
+
+@pytest.fixture
+def mixture_xgblss():
+    params = {"learning_rate": 0.1, "n_estimators": 10}
+    return XGBLSSRegressor(Mixture(Gaussian()), **params)
+
+
+@pytest.fixture
+def flow_xgblss():
+    params = {"learning_rate": 0.1, "n_estimators": 10}
+    spline_flow = SplineFlow(target_support="real", count_bins=2)
+    return XGBLSSRegressor(spline_flow, **params)
+
+
+@pytest.fixture
+def expectile_xgblss():
+    params = {
+        "learning_rate": 0.7298897353706068,
+        "max_depth": 2,
+        "gamma": 5.90940257278992e-06,
+        "subsample": 0.9810129322454306,
+        "colsample_bytree": 0.9546244491014185,
+        "min_child_weight": 113.32324947486019,
+        "booster": "gbtree",
+    }
+
+    return XGBLSSRegressor(Expectile(), **params)
+
+
+class TestClass:
+    def test_model_univ_train(self, univariate_data, univariate_xgblss):
+        # Unpack
+        X_train, y_train, _, _ = univariate_data
+        xgblss = univariate_xgblss
+
+        # Train the model
+        xgblss.fit(X_train, y_train)
+
+        # Assertions
+        assert isinstance(xgblss._Booster, Booster)
+        assert isinstance(xgblss._BoosterLSS, XGBoostLSS)
+
+    def test_model_mixture_train(self, univariate_data, mixture_xgblss):
+        # Unpack
+        X_train, y_train, _, _ = univariate_data
+        xgblss = mixture_xgblss
+
+        # Train the model
+        xgblss.fit(X_train, y_train)
+
+        # Assertions
+        assert isinstance(xgblss._Booster, Booster)
+        assert isinstance(xgblss._BoosterLSS, XGBoostLSS)
+
+    def test_model_flow_train(self, univariate_data, flow_xgblss):
+        # Unpack
+        X_train, y_train, _, _ = univariate_data
+        xgblss = flow_xgblss
+
+        # Train the model
+        xgblss.fit(X_train, y_train)
+
+        # Assertions
+        assert isinstance(xgblss._Booster, Booster)
+        assert isinstance(xgblss._BoosterLSS, XGBoostLSS)
+
+    def test_model_expectile_train(self, univariate_data, expectile_xgblss):
+        # Unpack
+        X_train, y_train, _, _ = univariate_data
+        xgblss = expectile_xgblss
+
+        # Train the model
+        xgblss.fit(X_train, y_train)
+
+        # Assertions
+        assert isinstance(xgblss._Booster, Booster)
+        assert isinstance(xgblss._BoosterLSS, XGBoostLSS)
+
+    def test_model_predict(self, univariate_data, univariate_xgblss):
+        # Unpack
+        X_train, y_train, X_test, y_test = univariate_data
+        # opt_params, n_rounds = univariate_params
+        xgblss = univariate_xgblss
+
+        # Train the model
+        xgblss.fit(X_train, y_train)
+
+        # Call the predict method
+        n_samples = 100
+        quantiles = [0.1, 0.5, 0.9]
+
+        pred_params = xgblss.predict(X_test, pred_type="parameters")
+        pred_samples = xgblss.predict(
+            X_test, pred_type="samples", n_samples=n_samples
+        )
+        pred_quantiles = xgblss.predict(
+            X_test, pred_type="quantiles", quantiles=quantiles
+        )
+
+        # Assertions
+        assert isinstance(pred_params, (pd.DataFrame, type(None)))
+        assert not pred_params.isna().any().any()
+        assert not np.isinf(pred_params).any().any()
+        assert pred_params.shape[1] == xgblss.dist.n_dist_param
+        assert approx(pred_params["loc"].mean(), abs=0.2) == 10.0
+
+        assert isinstance(pred_samples, (pd.DataFrame, type(None)))
+        assert not pred_samples.isna().any().any()
+        assert not np.isinf(pred_samples).any().any()
+        assert pred_samples.shape[1] == n_samples
+
+        assert isinstance(pred_quantiles, (pd.DataFrame, type(None)))
+        assert not pred_quantiles.isna().any().any()
+        assert not np.isinf(pred_quantiles).any().any()
+        assert pred_quantiles.shape[1] == len(quantiles)
diff --git a/xgboostlss/datasets/data_loader.py b/xgboostlss/datasets/data_loader.py
index ce5c9b78..11f66727 100644
--- a/xgboostlss/datasets/data_loader.py
+++ b/xgboostlss/datasets/data_loader.py
@@ -21,6 +21,15 @@ def load_simulated_gaussian_data():
     return train_df, test_df
 
 
+def generate_simulated_gaussian_data(X):
+    """Generate the true scale of the Gaussian distribution."""
+    return (
+        1
+        + 4 * ((0.3 < X["x_true"].values) & (X["x_true"].values < 0.5))
+        + 2 * (X["x_true"].values > 0.7)
+    )
+
+
 def load_simulated_studentT_data():
     """
     Returns train/test dataframe of a simulated example.
@@ -92,4 +101,4 @@ def load_articlake_data():
     data_path = pkg_resources.resource_stream(__name__, "arcticlake.csv")
     data_df = pd.read_csv(data_path)
 
-    return data_df
\ No newline at end of file
+    return data_df
diff --git a/xgboostlss/distributions/distribution_utils.py b/xgboostlss/distributions/distribution_utils.py
index 790aa0cc..b53548ed 100644
--- a/xgboostlss/distributions/distribution_utils.py
+++ b/xgboostlss/distributions/distribution_utils.py
@@ -339,7 +339,8 @@ def predict_dist(self,
                      pred_type: str = "parameters",
                      n_samples: int = 1000,
                      quantiles: list = [0.1, 0.5, 0.9],
-                     seed: str = 123
+                     seed: str = 123,
+                     **kwargs,
                      ) -> pd.DataFrame:
         """
         Function that predicts from the trained model.
@@ -374,7 +375,7 @@ def predict_dist(self,
         base_margin_test = (np.ones(shape=(data.num_row(), 1))) * start_values
         data.set_base_margin(base_margin_test.flatten())
 
-        predt = np.array(booster.predict(data, output_margin=True)).reshape(-1, self.n_dist_param)
+        predt = np.array(booster.predict(data, output_margin=True, **kwargs)).reshape(-1, self.n_dist_param)
         predt = torch.tensor(predt, dtype=torch.float32)
 
         # Transform predicted parameters to response scale
diff --git a/xgboostlss/model.py b/xgboostlss/model.py
index 131dd822..04b172d6 100644
--- a/xgboostlss/model.py
+++ b/xgboostlss/model.py
@@ -2,7 +2,7 @@
 import numpy as np
 import xgboost as xgb
 from xgboost.core import (
-    Booster, 
+    Booster,
     DMatrix,
 )
 
@@ -21,7 +21,6 @@
 from xgboostlss.utils import *
 import optuna
 from optuna.samplers import TPESampler
-import shap
 from typing import Any, Dict, Optional, Sequence, Tuple, Union
 
 
@@ -119,91 +118,92 @@ def train(
             xgb_model: Optional[Union[str, os.PathLike, Booster, bytearray]] = None,
             callbacks: Optional[Sequence[TrainingCallback]] = None,
     ) -> Booster:
-            """
-            Train a booster with given parameters.
-
-            Arguments
-            ---------
-            params :
-                Booster params.
-            dtrain :
-                Data to be trained.
-            num_boost_round :
-                Number of boosting iterations.
-            evals :
-                List of validation sets for which metrics will evaluated during training.
-                Validation metrics will help us track the performance of the model.
-            early_stopping_rounds :
-                Activates early stopping. Validation metric needs to improve at least once in
-                every **early_stopping_rounds** round(s) to continue training.
-                Requires at least one item in **evals**.
-                The method returns the model from the last iteration (not the best one).  Use
-                custom callback or model slicing if the best model is desired.
-                If there's more than one item in **evals**, the last entry will be used for early
-                stopping.
-                If there's more than one metric in the **eval_metric** parameter given in
-                **params**, the last metric will be used for early stopping.
-                If early stopping occurs, the model will have two additional fields:
-                ``bst.best_score``, ``bst.best_iteration``.
-            evals_result :
-                This dictionary stores the evaluation results of all the items in watchlist.
-                Example: with a watchlist containing
-                ``[(dtest,'eval'), (dtrain,'train')]`` and
-                a parameter containing ``('eval_metric': 'logloss')``,
-                the **evals_result** returns
-                .. code-block:: python
-                    {'train': {'logloss': ['0.48253', '0.35953']},
-                     'eval': {'logloss': ['0.480385', '0.357756']}}
-            verbose_eval :
-                Requires at least one item in **evals**.
-                If **verbose_eval** is True then the evaluation metric on the validation set is
-                printed at each boosting stage.
-                If **verbose_eval** is an integer then the evaluation metric on the validation set
-                is printed at every given **verbose_eval** boosting stage. The last boosting stage
-                / the boosting stage found by using **early_stopping_rounds** is also printed.
-                Example: with ``verbose_eval=4`` and at least one item in **evals**, an evaluation metric
-                is printed every 4 boosting stages, instead of every boosting stage.
-            xgb_model :
-                Xgb model to be loaded before training (allows training continuation).
-            callbacks :
-                List of callback functions that are applied at end of each iteration.
-                It is possible to use predefined callbacks by using
-                :ref:`Callback API <callback_api>`.
-                .. note::
-                   States in callback are not preserved during training, which means callback
-                   objects can not be reused for multiple training sessions without
-                   reinitialization or deepcopy.
-                .. code-block:: python
-                    for params in parameters_grid:
-                        # be sure to (re)initialize the callbacks before each run
-                        callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]
-                        xgboost.train(params, Xy, callbacks=callbacks)
-
-            Returns
-            -------
-            Booster:
-                The trained booster model.
-            """
-            self.set_params_adj(params)
-            self.adjust_labels(dtrain)
-            self.set_base_margin(dtrain)
-
-            # Set base_margin for evals
-            if evals is not None:
-                evals = self.set_eval_margin(evals, self.start_values)
-
-            self.booster = xgb.train(params,
-                                     dtrain,
-                                     num_boost_round=num_boost_round,
-                                     evals=evals,
-                                     obj=self.dist.objective_fn,
-                                     custom_metric=self.dist.metric_fn,
-                                     xgb_model=xgb_model,
-                                     callbacks=callbacks,
-                                     verbose_eval=verbose_eval,
-                                     evals_result=evals_result,
-                                     maximize=False,
-                                     early_stopping_rounds=early_stopping_rounds)
+        """
+        Train a booster with given parameters.
+
+        Arguments
+        ---------
+        params :
+            Booster params.
+        dtrain :
+            Data to be trained.
+        num_boost_round :
+            Number of boosting iterations.
+        evals :
+            List of validation sets for which metrics will evaluated during training.
+            Validation metrics will help us track the performance of the model.
+        early_stopping_rounds :
+            Activates early stopping. Validation metric needs to improve at least once in
+            every **early_stopping_rounds** round(s) to continue training.
+            Requires at least one item in **evals**.
+            The method returns the model from the last iteration (not the best one).  Use
+            custom callback or model slicing if the best model is desired.
+            If there's more than one item in **evals**, the last entry will be used for early
+            stopping.
+            If there's more than one metric in the **eval_metric** parameter given in
+            **params**, the last metric will be used for early stopping.
+            If early stopping occurs, the model will have two additional fields:
+            ``bst.best_score``, ``bst.best_iteration``.
+        evals_result :
+            This dictionary stores the evaluation results of all the items in watchlist.
+            Example: with a watchlist containing
+            ``[(dtest,'eval'), (dtrain,'train')]`` and
+            a parameter containing ``('eval_metric': 'logloss')``,
+            the **evals_result** returns
+            .. code-block:: python
+                {'train': {'logloss': ['0.48253', '0.35953']},
+                    'eval': {'logloss': ['0.480385', '0.357756']}}
+        verbose_eval :
+            Requires at least one item in **evals**.
+            If **verbose_eval** is True then the evaluation metric on the validation set is
+            printed at each boosting stage.
+            If **verbose_eval** is an integer then the evaluation metric on the validation set
+            is printed at every given **verbose_eval** boosting stage. The last boosting stage
+            / the boosting stage found by using **early_stopping_rounds** is also printed.
+            Example: with ``verbose_eval=4`` and at least one item in **evals**, an evaluation metric
+            is printed every 4 boosting stages, instead of every boosting stage.
+        xgb_model :
+            Xgb model to be loaded before training (allows training continuation).
+        callbacks :
+            List of callback functions that are applied at end of each iteration.
+            It is possible to use predefined callbacks by using
+            :ref:`Callback API <callback_api>`.
+            .. note::
+                States in callback are not preserved during training, which means callback
+                objects can not be reused for multiple training sessions without
+                reinitialization or deepcopy.
+            .. code-block:: python
+                for params in parameters_grid:
+                    # be sure to (re)initialize the callbacks before each run
+                    callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]
+                    xgboost.train(params, Xy, callbacks=callbacks)
+
+        Returns
+        -------
+        Booster:
+            The trained booster model.
+    """
+        self.set_params_adj(params)
+        self.adjust_labels(dtrain)
+        self.set_base_margin(dtrain)
+
+        # Set base_margin for evals
+        if evals is not None:
+            evals = self.set_eval_margin(evals, self.start_values)
+
+        self.booster = xgb.train(
+            params,
+            dtrain,
+            num_boost_round=num_boost_round,
+            evals=evals,
+            obj=self.dist.objective_fn,
+            custom_metric=self.dist.metric_fn,
+            xgb_model=xgb_model,
+            callbacks=callbacks,
+            verbose_eval=verbose_eval,
+            evals_result=evals_result,
+            maximize=False,
+            early_stopping_rounds=early_stopping_rounds)
 
     def cv(
         self,
@@ -468,7 +468,8 @@ def predict(self,
                 pred_type: str = "parameters",
                 n_samples: int = 1000,
                 quantiles: list = [0.1, 0.5, 0.9],
-                seed: str = 123):
+                seed: str = 123,
+                **kwargs):
         """
         Function that predicts from the trained model.
 
@@ -502,7 +503,8 @@ def predict(self,
                                           pred_type=pred_type,
                                           n_samples=n_samples,
                                           quantiles=quantiles,
-                                          seed=seed)
+                                          seed=seed,
+                                          **kwargs)
 
         return predt_df
 
@@ -530,6 +532,11 @@ def plot(self,
                 "Partial_Dependence" plots the partial dependence of the parameter on the feature.
                 "Feature_Importance" plots the feature importance of the parameter.
         """
+        try:
+            import shap
+        except ImportError:
+            raise ImportError("Please install shap to use this function.")
+
         shap.initjs()
         explainer = shap.TreeExplainer(self.booster)
         shap_values = explainer(X)
@@ -567,6 +574,10 @@ def expectile_plot(self,
             Specifies which SHapley-plot to visualize. Currently, "Partial_Dependence" and "Feature_Importance"
             are supported.
         """
+        try:
+            import shap
+        except ImportError:
+            raise ImportError("Please install shap to use this function.")
 
         shap.initjs()
         explainer = shap.TreeExplainer(self.booster)
diff --git a/xgboostlss/sklearn.py b/xgboostlss/sklearn.py
new file mode 100644
index 00000000..935030e5
--- /dev/null
+++ b/xgboostlss/sklearn.py
@@ -0,0 +1,501 @@
+""""""
+
+import numpy as np
+import pandas as pd
+
+from xgboost import XGBModel
+
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+)
+
+from xgboost.core import (
+    Booster,
+    DMatrix,
+    Metric,
+)
+
+from xgboost.config import config_context
+
+from xgboost.sklearn import _wrap_evaluation_matrices
+from xgboost.compat import SKLEARN_INSTALLED, XGBRegressorBase
+from xgboost._typing import ArrayLike, FeatureTypes
+from xgboost.callback import TrainingCallback
+
+from xgboostlss.model import XGBoostLSS
+from xgboostlss.distributions.Gaussian import Gaussian
+# Do not use class names on scikit-learn directly.  Re-define the classes on
+# .compat to guarantee the behavior without scikit-learning installed.
+
+
+class XGBModelLSS(XGBModel):
+    def __init__(
+        self,
+        dist: Optional[int] = None,
+        max_depth: Optional[int] = None,
+        max_leaves: Optional[int] = None,
+        max_bin: Optional[int] = None,
+        grow_policy: Optional[str] = None,
+        learning_rate: Optional[float] = None,
+        n_estimators: Optional[int] = None,
+        verbosity: Optional[int] = None,
+        objective: None = None,
+        booster: Optional[str] = None,
+        tree_method: Optional[str] = None,
+        n_jobs: Optional[int] = None,
+        gamma: Optional[float] = None,
+        min_child_weight: Optional[float] = None,
+        max_delta_step: Optional[float] = None,
+        subsample: Optional[float] = None,
+        sampling_method: Optional[str] = None,
+        colsample_bytree: Optional[float] = None,
+        colsample_bylevel: Optional[float] = None,
+        colsample_bynode: Optional[float] = None,
+        reg_alpha: Optional[float] = None,
+        reg_lambda: Optional[float] = None,
+        scale_pos_weight: Optional[float] = None,
+        base_score: Optional[float] = 0,
+        random_state: Optional[
+            Union[np.random.RandomState, np.random.Generator, int]
+        ] = None,
+        missing: float = np.nan,
+        num_parallel_tree: Optional[int] = None,
+        monotone_constraints: Optional[Union[Dict[str, int], str]] = None,
+        interaction_constraints: Optional[Union[str, Sequence[Sequence[str]]]] = None,
+        importance_type: Optional[str] = None,
+        device: Optional[str] = None,
+        validate_parameters: Optional[bool] = None,
+        enable_categorical: bool = False,
+        feature_types: Optional[FeatureTypes] = None,
+        max_cat_to_onehot: Optional[int] = None,
+        max_cat_threshold: Optional[int] = None,
+        multi_strategy: Optional[str] = None,
+        eval_metric: Optional[Union[str, List[str], Callable]] = None,
+        early_stopping_rounds: Optional[int] = None,
+        callbacks: Optional[List[TrainingCallback]] = None,
+        **kwargs: Any
+    ) -> None:
+        if not SKLEARN_INSTALLED:
+            raise ImportError(
+                "sklearn needs to be installed in order to use this module"
+            )
+
+        if dist is not None:
+            self.dist = dist
+        else:
+            self.dist = Gaussian()
+
+        self.n_estimators = n_estimators
+
+        if objective is not None:
+            raise ValueError("XGBoostLSS does not support objective function")
+        else:
+            self.objective = objective
+
+        self.max_depth = max_depth
+        self.max_leaves = max_leaves
+        self.max_bin = max_bin
+        self.grow_policy = grow_policy
+        self.learning_rate = learning_rate
+        self.verbosity = verbosity
+        self.booster = booster
+        self.tree_method = tree_method
+        self.gamma = gamma
+        self.min_child_weight = min_child_weight
+        self.max_delta_step = max_delta_step
+        self.subsample = subsample
+        self.sampling_method = sampling_method
+        self.colsample_bytree = colsample_bytree
+        self.colsample_bylevel = colsample_bylevel
+        self.colsample_bynode = colsample_bynode
+        self.reg_alpha = reg_alpha
+        self.reg_lambda = reg_lambda
+        self.scale_pos_weight = scale_pos_weight
+
+        if base_score != 0:
+            raise ValueError("XGBoostLSS base_score must be 0.")
+        else:
+            self.base_score = 0
+
+        self.missing = missing
+        self.num_parallel_tree = num_parallel_tree
+        self.random_state = random_state
+        self.n_jobs = n_jobs
+        self.monotone_constraints = monotone_constraints
+        self.interaction_constraints = interaction_constraints
+        self.importance_type = importance_type
+        self.device = device
+        self.validate_parameters = validate_parameters
+        self.enable_categorical = enable_categorical
+        self.feature_types = feature_types
+        self.max_cat_to_onehot = max_cat_to_onehot
+        self.max_cat_threshold = max_cat_threshold
+        self.multi_strategy = multi_strategy
+        self.eval_metric = eval_metric
+        self.early_stopping_rounds = early_stopping_rounds
+        self.callbacks = callbacks
+        self.kwargs = kwargs
+
+        self.start_values = None  # Starting values for distributional parameters
+        self.multivariate_label_expand = False
+        self.multivariate_eval_label_expand = False
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        *,
+        sample_weight: Optional[ArrayLike] = None,
+        base_margin: Optional[ArrayLike] = None,
+        eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
+        eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
+        early_stopping_rounds: Optional[int] = None,
+        verbose: Optional[Union[bool, int]] = True,
+        xgb_model: Optional[Union[Booster, str, "XGBModel"]] = None,
+        sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
+        base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
+        feature_weights: Optional[ArrayLike] = None,
+        callbacks: Optional[Sequence[TrainingCallback]] = None,
+    ) -> "XGBModel":
+        with config_context(verbosity=self.verbosity):
+            evals_result: TrainingCallback.EvalsLog = {}
+
+            train_dmatrix, evals = _wrap_evaluation_matrices(
+                missing=self.missing,
+                X=X,
+                y=y,
+                group=None,
+                qid=None,
+                sample_weight=sample_weight,
+                base_margin=base_margin,
+                feature_weights=feature_weights,
+                eval_set=eval_set,
+                sample_weight_eval_set=sample_weight_eval_set,
+                base_margin_eval_set=base_margin_eval_set,
+                eval_group=None,
+                eval_qid=None,
+                create_dmatrix=self._create_dmatrix,
+                enable_categorical=self.enable_categorical,
+                feature_types=self.feature_types,
+            )
+
+            params = self.get_xgb_params()
+            params.pop("dist", None)
+
+            params_adj = {
+                "num_target": self.dist.n_dist_param,
+                "disable_default_eval_metric": True
+            }
+
+            params.update(params_adj)
+            evals = None if not bool(evals) else evals
+
+            self._BoosterLSS = XGBoostLSS(self.dist)
+            self._BoosterLSS.train(
+                params,
+                train_dmatrix,
+                self.get_num_boosting_rounds(),
+                evals=evals,
+                early_stopping_rounds=early_stopping_rounds,
+                evals_result=evals_result,
+                verbose_eval=verbose,
+                # xgb_model=self._Booster.booster,
+                callbacks=callbacks,
+            )
+
+            self._Booster = self._BoosterLSS.booster
+
+    def predict(
+        self,
+        X: ArrayLike,
+        pred_type: str = "parameters",
+        quantiles: Optional[Union[List[float], float]] = None,
+        n_samples: Optional[int] = None,
+        validate_features: bool = True,
+        base_margin: Optional[ArrayLike] = None,
+    ) -> ArrayLike:
+
+        with config_context(verbosity=self.verbosity):
+            n_samples_ = n_samples or 1000
+
+            test = DMatrix(
+                X,
+                base_margin=base_margin,
+                missing=self.missing,
+                nthread=self.n_jobs,
+                feature_types=self.feature_types,
+                enable_categorical=self.enable_categorical,
+            )
+            y_pred = self._BoosterLSS.predict(
+                data=test,
+                pred_type=pred_type,
+                quantiles=quantiles,
+                n_samples=n_samples_,
+                validate_features=validate_features,
+            )
+
+            if isinstance(y_pred, pd.DataFrame):
+                return y_pred.values
+            else:
+                return y_pred
+
+
+class XGBLSSRegressor(XGBModelLSS, XGBRegressorBase):
+    """
+    Implementation of the scikit-learn API for XGBoostLSS.
+
+    Parameters
+    ----------
+    dist : Distribution
+        DistributionClass object.  Default is Gaussian.
+
+    max_depth :  Optional[int]
+        Maximum tree depth for base learners.
+    max_leaves :
+        Maximum number of leaves; 0 indicates no limit.
+    max_bin :
+        If using histogram-based algorithm, maximum number of bins per feature
+    grow_policy :
+        Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow
+        depth-wise. 1: favor splitting at nodes with highest loss change.
+    learning_rate : Optional[float]
+        Boosting learning rate (xgb's "eta")
+    verbosity : Optional[int]
+        The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
+
+    booster: Optional[str]
+        Specify which booster to use: `gbtree`, `gblinear` or `dart`.
+    tree_method: Optional[str]
+        Specify which tree method to use.  Default to auto.  If this parameter is set to
+        default, XGBoost will choose the most conservative option available.  It's
+        recommended to study this option from the parameters document :doc:`tree method
+        </treemethod>`
+    n_jobs : Optional[int]
+        Number of parallel threads used to run xgboost.  When used with other
+        Scikit-Learn algorithms like grid search, you may choose which algorithm to
+        parallelize and balance the threads.  Creating thread contention will
+        significantly slow down both algorithms.
+    gamma : Optional[float]
+        (min_split_loss) Minimum loss reduction required to make a further partition on a
+        leaf node of the tree.
+    min_child_weight : Optional[float]
+        Minimum sum of instance weight(hessian) needed in a child.
+    max_delta_step : Optional[float]
+        Maximum delta step we allow each tree's weight estimation to be.
+    subsample : Optional[float]
+        Subsample ratio of the training instance.
+    sampling_method :
+        Sampling method. Used only by the GPU version of ``hist`` tree method.
+          - ``uniform``: select random training instances uniformly.
+          - ``gradient_based`` select random training instances with higher probability
+            when the gradient and hessian are larger. (cf. CatBoost)
+    colsample_bytree : Optional[float]
+        Subsample ratio of columns when constructing each tree.
+    colsample_bylevel : Optional[float]
+        Subsample ratio of columns for each level.
+    colsample_bynode : Optional[float]
+        Subsample ratio of columns for each split.
+    reg_alpha : Optional[float]
+        L1 regularization term on weights (xgb's alpha).
+    reg_lambda : Optional[float]
+        L2 regularization term on weights (xgb's lambda).
+    scale_pos_weight : Optional[float]
+        Balancing of positive and negative weights.
+    base_score : Optional[float]
+        The initial prediction score of all instances, global bias.
+    random_state : Optional[Union[numpy.random.RandomState, numpy.random.Generator, int]]
+        Random number seed.
+
+        .. note::
+
+           Using gblinear booster with shotgun updater is nondeterministic as
+           it uses Hogwild algorithm.
+
+    missing : float, default np.nan
+        Value in the data which needs to be present as a missing value.
+    num_parallel_tree: Optional[int]
+        Used for boosting random forest.
+    monotone_constraints : Optional[Union[Dict[str, int], str]]
+        Constraint of variable monotonicity.  See :doc:`tutorial </tutorials/monotonic>`
+        for more information.
+    interaction_constraints : Optional[Union[str, List[Tuple[str]]]]
+        Constraints for interaction representing permitted interactions.  The
+        constraints must be specified in the form of a nested list, e.g. ``[[0, 1], [2,
+        3, 4]]``, where each inner list is a group of indices of features that are
+        allowed to interact with each other.  See :doc:`tutorial
+        </tutorials/feature_interaction_constraint>` for more information
+    importance_type: Optional[str]
+        The feature importance type for the feature_importances\\_ property:
+
+        * For tree model, it's either "gain", "weight", "cover", "total_gain" or
+          "total_cover".
+        * For linear model, only "weight" is defined and it's the normalized coefficients
+          without bias.
+
+    device : Optional[str]
+
+        .. versionadded:: 2.0.0
+
+        Device ordinal, available options are `cpu`, `cuda`, and `gpu`.
+
+    validate_parameters : Optional[bool]
+
+        Give warnings for unknown parameter.
+
+    enable_categorical : bool
+
+        See the same parameter of :py:class:`DMatrix` for details.
+
+    feature_types : Optional[FeatureTypes]
+
+        .. versionadded:: 1.7.0
+
+        Used for specifying feature types without constructing a dataframe. See
+        :py:class:`DMatrix` for details.
+
+    max_cat_to_onehot : Optional[int]
+
+        .. versionadded:: 1.6.0
+
+        .. note:: This parameter is experimental
+
+        A threshold for deciding whether XGBoost should use one-hot encoding based split
+        for categorical data.  When number of categories is lesser than the threshold
+        then one-hot encoding is chosen, otherwise the categories will be partitioned
+        into children nodes. Also, `enable_categorical` needs to be set to have
+        categorical feature support. See :doc:`Categorical Data
+        </tutorials/categorical>` and :ref:`cat-param` for details.
+
+    max_cat_threshold : Optional[int]
+
+        .. versionadded:: 1.7.0
+
+        .. note:: This parameter is experimental
+
+        Maximum number of categories considered for each split. Used only by
+        partition-based splits for preventing over-fitting. Also, `enable_categorical`
+        needs to be set to have categorical feature support. See :doc:`Categorical Data
+        </tutorials/categorical>` and :ref:`cat-param` for details.
+
+    multi_strategy : Optional[str]
+
+        .. versionadded:: 2.0.0
+
+        .. note:: This parameter is working-in-progress.
+
+        The strategy used for training multi-target models, including multi-target
+        regression and multi-class classification. See :doc:`/tutorials/multioutput` for
+        more information.
+
+        - ``one_output_per_tree``: One model for each target.
+        - ``multi_output_tree``:  Use multi-target trees.
+
+    eval_metric : Optional[Union[str, List[str], Callable]]
+
+        .. versionadded:: 1.6.0
+
+        Metric used for monitoring the training result and early stopping.  It can be a
+        string or list of strings as names of predefined metric in XGBoost (See
+        doc/parameter.rst), one of the metrics in :py:mod:`sklearn.metrics`, or any
+        other user defined metric that looks like `sklearn.metrics`.
+
+        If custom objective is also provided, then custom metric should implement the
+        corresponding reverse link function.
+
+        Unlike the `scoring` parameter commonly used in scikit-learn, when a callable
+        object is provided, it's assumed to be a cost function and by default XGBoost
+        will minimize the result during early stopping.
+
+        For advanced usage on Early stopping like directly choosing to maximize instead
+        of minimize, see :py:obj:`xgboost.callback.EarlyStopping`.
+
+        See :doc:`/tutorials/custom_metric_obj` and :ref:`custom-obj-metric` for more
+        information.
+
+        .. note::
+
+             This parameter replaces `eval_metric` in :py:meth:`fit` method.  The old
+             one receives un-transformed prediction regardless of whether custom
+             objective is being used.
+
+        .. code-block:: python
+
+            from sklearn.datasets import load_diabetes
+            from sklearn.metrics import mean_absolute_error
+            X, y = load_diabetes(return_X_y=True)
+            reg = xgb.XGBRegressor(
+                tree_method="hist",
+                eval_metric=mean_absolute_error,
+            )
+            reg.fit(X, y, eval_set=[(X, y)])
+
+    early_stopping_rounds : Optional[int]
+
+        .. versionadded:: 1.6.0
+
+        - Activates early stopping. Validation metric needs to improve at least once in
+          every **early_stopping_rounds** round(s) to continue training.  Requires at
+          least one item in **eval_set** in :py:meth:`fit`.
+
+        - If early stopping occurs, the model will have two additional attributes:
+          :py:attr:`best_score` and :py:attr:`best_iteration`. These are used by the
+          :py:meth:`predict` and :py:meth:`apply` methods to determine the optimal
+          number of trees during inference. If users want to access the full model
+          (including trees built after early stopping), they can specify the
+          `iteration_range` in these inference methods. In addition, other utilities
+          like model plotting can also use the entire model.
+
+        - If you prefer to discard the trees after `best_iteration`, consider using the
+          callback function :py:class:`xgboost.callback.EarlyStopping`.
+
+        - If there's more than one item in **eval_set**, the last entry will be used for
+          early stopping.  If there's more than one metric in **eval_metric**, the last
+          metric will be used for early stopping.
+
+        .. note::
+
+            This parameter replaces `early_stopping_rounds` in :py:meth:`fit` method.
+
+    callbacks : Optional[List[TrainingCallback]]
+        List of callback functions that are applied at end of each iteration.
+        It is possible to use predefined callbacks by using
+        :ref:`Callback API <callback_api>`.
+
+        .. note::
+
+           States in callback are not preserved during training, which means callback
+           objects can not be reused for multiple training sessions without
+           reinitialization or deepcopy.
+
+        .. code-block:: python
+
+            for params in parameters_grid:
+                # be sure to (re)initialize the callbacks before each run
+                callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]
+                reg = xgboost.XGBRegressor(**params, callbacks=callbacks)
+                reg.fit(X, y)
+
+    kwargs : dict, optional
+        Keyword arguments for XGBoost Booster object.  Full documentation of parameters
+        can be found :doc:`here </parameter>`.
+        Attempting to set a parameter via the constructor args and \\*\\*kwargs
+        dict simultaneously will result in a TypeError.
+
+        .. note:: \\*\\*kwargs unsupported by scikit-learn
+
+            \\*\\*kwargs is unsupported by scikit-learn.  We do not guarantee
+            that parameters passed via this argument will interact properly
+            with scikit-learn.
+    """
+    def __init__(
+        self, **kwargs: Any
+    ) -> None:
+        """Some docs here."""
+        super().__init__(**kwargs)
diff --git a/xgboostlss/utils.py b/xgboostlss/utils.py
index 63d946bb..6a5335e0 100644
--- a/xgboostlss/utils.py
+++ b/xgboostlss/utils.py
@@ -245,7 +245,7 @@ def gumbel_softmax_fn(predt: torch.tensor,
     Gumbel-softmax function used to ensure predt is adding to one.
 
     The Gumbel-softmax distribution is a continuous distribution over the simplex, which can be thought of as a "soft"
-    version of a categorical distribution. It’s a way to draw samples from a categorical distribution in a
+    version of a categorical distribution. It's a way to draw samples from a categorical distribution in a
     differentiable way. The motivation behind using the Gumbel-Softmax is to make the discrete sampling process of
     categorical variables differentiable, which is useful in gradient-based optimization problems. To sample from a
     Gumbel-Softmax distribution, one would use the Gumbel-max trick: add a Gumbel noise to logits and apply the softmax.