All_Features-kNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import sklearn.preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, plot_confusion_matrix\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import sklearn.pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from transforms import FourierTransform, MeanTransform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "from dataset import HARDatasetCrops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "dataset = HARDatasetCrops('motionsense-dataset/train', 256, 50, 50, metadata_file='motionsense-dataset/data_subjects_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size: 4095\n",
      "Datapoints shape: (256, 12)\n"
     ]
    }
   ],
   "source": [
    "print('Dataset size:', len(dataset))\n",
    "\n",
    "sample = dataset[0][0]\n",
    "print('Datapoints shape:', sample.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset is composed of 5205 datapoints, each have shape `(256, 12)` because we have 12 signals of 256 samples each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "X_roll = np.array([sample[:,0] for sample, _, _ in dataset])\n",
    "X_pitch = np.array([sample[:,1] for sample, _, _ in dataset])\n",
    "X_yaw = np.array([sample[:,2] for sample, _, _ in dataset])\n",
    "X_gx = np.array([sample[:,3] for sample, _, _ in dataset])\n",
    "X_gy = np.array([sample[:,4] for sample, _, _ in dataset])\n",
    "X_gz = np.array([sample[:,5] for sample, _, _ in dataset])\n",
    "X_acc = np.array([np.linalg.norm(sample[:,6:9], axis=1) for sample, _, _ in dataset])\n",
    "X_rot = np.array([np.linalg.norm(sample[:,9:12], axis=1) for sample, _, _ in dataset])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classes are the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to fit a `sklearn` model we should encode each class with a unique integer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "label_encoder = sklearn.preprocessing.LabelEncoder()\n",
    "label_encoder.fit(list(dataset.CLASSES.keys()));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "y = label_encoder.transform([cls for _, cls, _ in dataset])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train-test splitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first \"stack\" our features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4095, 2048)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.hstack([X_roll,\n",
    "               X_pitch,\n",
    "               X_yaw,\n",
    "               X_gx,\n",
    "               X_gy,\n",
    "               X_gz,\n",
    "               X_acc,\n",
    "               X_rot])\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = np.array([metadata for _, _, metadata in dataset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4095, 4)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.hstack([X, metadata])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA feature reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ColumnTransformer([\n",
    "    (\"roll\", roll_transform, slice(0, 256)),\n",
    "    (\"pitch\", pitch_transform, slice(256, 512)),\n",
    "    (\"yaw\", yaw_transform, slice(512, 768)),\n",
    "    (\"gravity-x\", gx_transform, slice(768, 1024)),\n",
    "    (\"gravity-y\", gy_transform, slice(1024, 1280)),\n",
    "    (\"gravity-z\", gz_transform, slice(1280, 1536)),\n",
    "    (\"accelaration\", acc_transform, slice(1536, 1792)),\n",
    "    (\"rotation\", rot_transform, slice(1792, 2048)),\n",
    "    (\"metadata\", \"passthrough\", slice(-4, X.shape[1]))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification with k-Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import sklearn.neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = ShuffleSplit(n_splits=5, test_size=0.15, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = sklearn.pipeline.Pipeline([\n",
    "    ('features', features),\n",
    "    ('normalize', sklearn.preprocessing.StandardScaler()),\n",
    "    ('model', sklearn.neighbors.KNeighborsClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid =  {\n",
    "    'normalize': ['passthrough', sklearn.preprocessing.StandardScaler()],\n",
    "    'model__n_neighbors': [3, 5, 7, 9, 11],\n",
    "    'model__weights': ['uniform', 'distance']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=5, random_state=0, test_size=0.15, train_size=None),\n",
       "             error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('features',\n",
       "                                        ColumnTransformer(n_jobs=None,\n",
       "                                                          remainder='drop',\n",
       "                                                          sparse_threshold=0.3,\n",
       "                                                          transformer_weights=None,\n",
       "                                                          transformers=[('roll',\n",
       "                                                                         Pipeline(memory=None,\n",
       "                                                                                  steps=[('meantransform',\n",
       "                                                                                          MeanTransform())],\n",
       "                                                                                  verbose=False),\n",
       "                                                                         slic...\n",
       "                                                             metric_params=None,\n",
       "                                                             n_jobs=None,\n",
       "                                                             n_neighbors=5, p=2,\n",
       "                                                             weights='uniform'))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'model__n_neighbors': [3, 5, 7, 9, 11],\n",
       "                         'model__weights': ['uniform', 'distance'],\n",
       "                         'normalize': ['passthrough',\n",
       "                                       StandardScaler(copy=True, with_mean=True,\n",
       "                                                      with_std=True)]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(clf, param_grid, cv=cv)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model__n_neighbors': 3,\n",
       " 'model__weights': 'distance',\n",
       " 'normalize': 'passthrough'}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9524271844660195"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results = pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>passthrough</td>\n",
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       "      <td>0.946602</td>\n",
       "      <td>0.966019</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.950971</td>\n",
       "      <td>0.013592</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.115071</td>\n",
       "      <td>0.015295</td>\n",
       "      <td>0.171992</td>\n",
       "      <td>0.006856</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 3, 'model__weights': 'u...</td>\n",
       "      <td>0.905340</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.907767</td>\n",
       "      <td>0.905340</td>\n",
       "      <td>0.898058</td>\n",
       "      <td>0.908738</td>\n",
       "      <td>0.009781</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.107790</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>0.031617</td>\n",
       "      <td>0.001735</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 3, 'model__weights': 'd...</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.966019</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.963592</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.952427</td>\n",
       "      <td>0.012338</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.102202</td>\n",
       "      <td>0.004006</td>\n",
       "      <td>0.137602</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 3, 'model__weights': 'd...</td>\n",
       "      <td>0.905340</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.905340</td>\n",
       "      <td>0.910194</td>\n",
       "      <td>0.893204</td>\n",
       "      <td>0.909223</td>\n",
       "      <td>0.012714</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.100526</td>\n",
       "      <td>0.002316</td>\n",
       "      <td>0.066087</td>\n",
       "      <td>0.004273</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 5, 'model__weights': 'u...</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.922330</td>\n",
       "      <td>0.946117</td>\n",
       "      <td>0.013418</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.105456</td>\n",
       "      <td>0.002584</td>\n",
       "      <td>0.165969</td>\n",
       "      <td>0.005849</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 5, 'model__weights': 'u...</td>\n",
       "      <td>0.895631</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.890777</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.885922</td>\n",
       "      <td>0.899515</td>\n",
       "      <td>0.011133</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.106162</td>\n",
       "      <td>0.001714</td>\n",
       "      <td>0.035109</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 5, 'model__weights': 'd...</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.963592</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.011989</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.112887</td>\n",
       "      <td>0.006884</td>\n",
       "      <td>0.157320</td>\n",
       "      <td>0.007439</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 5, 'model__weights': 'd...</td>\n",
       "      <td>0.898058</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.888350</td>\n",
       "      <td>0.910194</td>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.898544</td>\n",
       "      <td>0.011528</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.113407</td>\n",
       "      <td>0.011327</td>\n",
       "      <td>0.068870</td>\n",
       "      <td>0.005734</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 7, 'model__weights': 'u...</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.915049</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.016246</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.109531</td>\n",
       "      <td>0.009070</td>\n",
       "      <td>0.170871</td>\n",
       "      <td>0.009922</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 7, 'model__weights': 'u...</td>\n",
       "      <td>0.881068</td>\n",
       "      <td>0.907767</td>\n",
       "      <td>0.871359</td>\n",
       "      <td>0.890777</td>\n",
       "      <td>0.868932</td>\n",
       "      <td>0.883981</td>\n",
       "      <td>0.014186</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.121033</td>\n",
       "      <td>0.011372</td>\n",
       "      <td>0.042802</td>\n",
       "      <td>0.007150</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 7, 'model__weights': 'd...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.919903</td>\n",
       "      <td>0.943204</td>\n",
       "      <td>0.015700</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.113796</td>\n",
       "      <td>0.010630</td>\n",
       "      <td>0.162864</td>\n",
       "      <td>0.010712</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 7, 'model__weights': 'd...</td>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.871359</td>\n",
       "      <td>0.890777</td>\n",
       "      <td>0.859223</td>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.018098</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.112477</td>\n",
       "      <td>0.007714</td>\n",
       "      <td>0.076667</td>\n",
       "      <td>0.009038</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 9, 'model__weights': 'u...</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.915049</td>\n",
       "      <td>0.937864</td>\n",
       "      <td>0.014285</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.115249</td>\n",
       "      <td>0.006755</td>\n",
       "      <td>0.188437</td>\n",
       "      <td>0.019073</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 9, 'model__weights': 'u...</td>\n",
       "      <td>0.866505</td>\n",
       "      <td>0.900485</td>\n",
       "      <td>0.856796</td>\n",
       "      <td>0.871359</td>\n",
       "      <td>0.849515</td>\n",
       "      <td>0.868932</td>\n",
       "      <td>0.017503</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.109848</td>\n",
       "      <td>0.009214</td>\n",
       "      <td>0.038133</td>\n",
       "      <td>0.006168</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 9, 'model__weights': 'd...</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.924757</td>\n",
       "      <td>0.943689</td>\n",
       "      <td>0.013505</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.100259</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>0.143639</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 9, 'model__weights': 'd...</td>\n",
       "      <td>0.868932</td>\n",
       "      <td>0.902913</td>\n",
       "      <td>0.856796</td>\n",
       "      <td>0.878641</td>\n",
       "      <td>0.847087</td>\n",
       "      <td>0.870874</td>\n",
       "      <td>0.019259</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.097449</td>\n",
       "      <td>0.002096</td>\n",
       "      <td>0.065603</td>\n",
       "      <td>0.002265</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 11, 'model__weights': '...</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.910194</td>\n",
       "      <td>0.930097</td>\n",
       "      <td>0.011112</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.099291</td>\n",
       "      <td>0.002356</td>\n",
       "      <td>0.163860</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 11, 'model__weights': '...</td>\n",
       "      <td>0.856796</td>\n",
       "      <td>0.885922</td>\n",
       "      <td>0.849515</td>\n",
       "      <td>0.868932</td>\n",
       "      <td>0.832524</td>\n",
       "      <td>0.858738</td>\n",
       "      <td>0.017994</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.103084</td>\n",
       "      <td>0.000953</td>\n",
       "      <td>0.037824</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'model__n_neighbors': 11, 'model__weights': '...</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.938835</td>\n",
       "      <td>0.014994</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.101470</td>\n",
       "      <td>0.002968</td>\n",
       "      <td>0.144898</td>\n",
       "      <td>0.003262</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'model__n_neighbors': 11, 'model__weights': '...</td>\n",
       "      <td>0.851942</td>\n",
       "      <td>0.881068</td>\n",
       "      <td>0.851942</td>\n",
       "      <td>0.878641</td>\n",
       "      <td>0.834951</td>\n",
       "      <td>0.859709</td>\n",
       "      <td>0.017597</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.118917      0.008651         0.066947        0.007273   \n",
       "1        0.115071      0.015295         0.171992        0.006856   \n",
       "2        0.107790      0.003365         0.031617        0.001735   \n",
       "3        0.102202      0.004006         0.137602        0.002551   \n",
       "4        0.100526      0.002316         0.066087        0.004273   \n",
       "5        0.105456      0.002584         0.165969        0.005849   \n",
       "6        0.106162      0.001714         0.035109        0.001629   \n",
       "7        0.112887      0.006884         0.157320        0.007439   \n",
       "8        0.113407      0.011327         0.068870        0.005734   \n",
       "9        0.109531      0.009070         0.170871        0.009922   \n",
       "10       0.121033      0.011372         0.042802        0.007150   \n",
       "11       0.113796      0.010630         0.162864        0.010712   \n",
       "12       0.112477      0.007714         0.076667        0.009038   \n",
       "13       0.115249      0.006755         0.188437        0.019073   \n",
       "14       0.109848      0.009214         0.038133        0.006168   \n",
       "15       0.100259      0.001287         0.143639        0.003959   \n",
       "16       0.097449      0.002096         0.065603        0.002265   \n",
       "17       0.099291      0.002356         0.163860        0.004830   \n",
       "18       0.103084      0.000953         0.037824        0.001373   \n",
       "19       0.101470      0.002968         0.144898        0.003262   \n",
       "\n",
       "   param_model__n_neighbors param_model__weights  \\\n",
       "0                         3              uniform   \n",
       "1                         3              uniform   \n",
       "2                         3             distance   \n",
       "3                         3             distance   \n",
       "4                         5              uniform   \n",
       "5                         5              uniform   \n",
       "6                         5             distance   \n",
       "7                         5             distance   \n",
       "8                         7              uniform   \n",
       "9                         7              uniform   \n",
       "10                        7             distance   \n",
       "11                        7             distance   \n",
       "12                        9              uniform   \n",
       "13                        9              uniform   \n",
       "14                        9             distance   \n",
       "15                        9             distance   \n",
       "16                       11              uniform   \n",
       "17                       11              uniform   \n",
       "18                       11             distance   \n",
       "19                       11             distance   \n",
       "\n",
       "                                      param_normalize  \\\n",
       "0                                         passthrough   \n",
       "1   StandardScaler(copy=True, with_mean=True, with...   \n",
       "2                                         passthrough   \n",
       "3   StandardScaler(copy=True, with_mean=True, with...   \n",
       "4                                         passthrough   \n",
       "5   StandardScaler(copy=True, with_mean=True, with...   \n",
       "6                                         passthrough   \n",
       "7   StandardScaler(copy=True, with_mean=True, with...   \n",
       "8                                         passthrough   \n",
       "9   StandardScaler(copy=True, with_mean=True, with...   \n",
       "10                                        passthrough   \n",
       "11  StandardScaler(copy=True, with_mean=True, with...   \n",
       "12                                        passthrough   \n",
       "13  StandardScaler(copy=True, with_mean=True, with...   \n",
       "14                                        passthrough   \n",
       "15  StandardScaler(copy=True, with_mean=True, with...   \n",
       "16                                        passthrough   \n",
       "17  StandardScaler(copy=True, with_mean=True, with...   \n",
       "18                                        passthrough   \n",
       "19  StandardScaler(copy=True, with_mean=True, with...   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'model__n_neighbors': 3, 'model__weights': 'u...           0.946602   \n",
       "1   {'model__n_neighbors': 3, 'model__weights': 'u...           0.905340   \n",
       "2   {'model__n_neighbors': 3, 'model__weights': 'd...           0.946602   \n",
       "3   {'model__n_neighbors': 3, 'model__weights': 'd...           0.905340   \n",
       "4   {'model__n_neighbors': 5, 'model__weights': 'u...           0.941748   \n",
       "5   {'model__n_neighbors': 5, 'model__weights': 'u...           0.895631   \n",
       "6   {'model__n_neighbors': 5, 'model__weights': 'd...           0.941748   \n",
       "7   {'model__n_neighbors': 5, 'model__weights': 'd...           0.898058   \n",
       "8   {'model__n_neighbors': 7, 'model__weights': 'u...           0.927184   \n",
       "9   {'model__n_neighbors': 7, 'model__weights': 'u...           0.881068   \n",
       "10  {'model__n_neighbors': 7, 'model__weights': 'd...           0.932039   \n",
       "11  {'model__n_neighbors': 7, 'model__weights': 'd...           0.883495   \n",
       "12  {'model__n_neighbors': 9, 'model__weights': 'u...           0.929612   \n",
       "13  {'model__n_neighbors': 9, 'model__weights': 'u...           0.866505   \n",
       "14  {'model__n_neighbors': 9, 'model__weights': 'd...           0.934466   \n",
       "15  {'model__n_neighbors': 9, 'model__weights': 'd...           0.868932   \n",
       "16  {'model__n_neighbors': 11, 'model__weights': '...           0.929612   \n",
       "17  {'model__n_neighbors': 11, 'model__weights': '...           0.856796   \n",
       "18  {'model__n_neighbors': 11, 'model__weights': '...           0.934466   \n",
       "19  {'model__n_neighbors': 11, 'model__weights': '...           0.851942   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.966019           0.953883           0.961165   \n",
       "1            0.927184           0.907767           0.905340   \n",
       "2            0.966019           0.953883           0.963592   \n",
       "3            0.932039           0.905340           0.910194   \n",
       "4            0.961165           0.951456           0.953883   \n",
       "5            0.912621           0.890777           0.912621   \n",
       "6            0.963592           0.953883           0.956311   \n",
       "7            0.912621           0.888350           0.910194   \n",
       "8            0.956311           0.941748           0.956311   \n",
       "9            0.907767           0.871359           0.890777   \n",
       "10           0.961165           0.944175           0.958738   \n",
       "11           0.912621           0.871359           0.890777   \n",
       "12           0.956311           0.941748           0.946602   \n",
       "13           0.900485           0.856796           0.871359   \n",
       "14           0.961165           0.941748           0.956311   \n",
       "15           0.902913           0.856796           0.878641   \n",
       "16           0.944175           0.934466           0.932039   \n",
       "17           0.885922           0.849515           0.868932   \n",
       "18           0.956311           0.941748           0.949029   \n",
       "19           0.881068           0.851942           0.878641   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.927184         0.950971        0.013592                2  \n",
       "1            0.898058         0.908738        0.009781               12  \n",
       "2            0.932039         0.952427        0.012338                1  \n",
       "3            0.893204         0.909223        0.012714               11  \n",
       "4            0.922330         0.946117        0.013418                4  \n",
       "5            0.885922         0.899515        0.011133               13  \n",
       "6            0.929612         0.949029        0.011989                3  \n",
       "7            0.883495         0.898544        0.011528               14  \n",
       "8            0.915049         0.939320        0.016246                7  \n",
       "9            0.868932         0.883981        0.014186               15  \n",
       "10           0.919903         0.943204        0.015700                6  \n",
       "11           0.859223         0.883495        0.018098               16  \n",
       "12           0.915049         0.937864        0.014285                9  \n",
       "13           0.849515         0.868932        0.017503               18  \n",
       "14           0.924757         0.943689        0.013505                5  \n",
       "15           0.847087         0.870874        0.019259               17  \n",
       "16           0.910194         0.930097        0.011112               10  \n",
       "17           0.832524         0.858738        0.017994               20  \n",
       "18           0.912621         0.938835        0.014994                8  \n",
       "19           0.834951         0.859709        0.017597               19  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dws       1.00      1.00      1.00       236\n",
      "         jog       1.00      1.00      1.00       265\n",
      "         sit       1.00      1.00      1.00       663\n",
      "         std       1.00      1.00      1.00       615\n",
      "         ups       1.00      1.00      1.00       298\n",
      "         wlk       1.00      1.00      1.00       666\n",
      "\n",
      "    accuracy                           1.00      2743\n",
      "   macro avg       1.00      1.00      1.00      2743\n",
      "weighted avg       1.00      1.00      1.00      2743\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, grid_search.predict(X_train), target_names=label_encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x13994c990>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(grid_search, X_train, y_train,\n",
    "                      display_labels=label_encoder.classes_,\n",
    "                      cmap=plt.cm.Blues,\n",
    "                      normalize=\"true\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dws       0.93      0.88      0.90       123\n",
      "         jog       0.99      0.98      0.99       119\n",
      "         sit       0.97      0.93      0.95       341\n",
      "         std       0.92      0.96      0.94       285\n",
      "         ups       0.91      0.92      0.92       138\n",
      "         wlk       0.96      0.98      0.97       346\n",
      "\n",
      "    accuracy                           0.95      1352\n",
      "   macro avg       0.95      0.94      0.95      1352\n",
      "weighted avg       0.95      0.95      0.95      1352\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_search.predict(X_test), target_names=label_encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x138436310>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(grid_search, X_test, y_test,\n",
    "                      display_labels=label_encoder.classes_,\n",
    "                      cmap=plt.cm.Blues,\n",
    "                      normalize=\"true\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class-balancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn.pipeline\n",
    "import imblearn.over_sampling\n",
    "import imblearn.under_sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = imblearn.pipeline.Pipeline([\n",
    "    ('features', features),\n",
    "    ('balancing', 'passthrough'),\n",
    "    ('normalize', sklearn.preprocessing.StandardScaler()),\n",
    "    ('model', sklearn.neighbors.KNeighborsClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_strategy = {\n",
    "    0: 300,\n",
    "    1: 300,\n",
    "    2: 300,\n",
    "    3: 300,\n",
    "    4: 300,\n",
    "    5: 300,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid =  {\n",
    "    'balancing': ['passthrough', imblearn.over_sampling.SMOTE(random_state=1), imblearn.under_sampling.RandomUnderSampler(sampling_strategy=sampling_strategy, replacement=True, random_state=1)],\n",
    "    'normalize': ['passthrough', sklearn.preprocessing.StandardScaler()],\n",
    "    'model__n_neighbors': [3, 5, 7, 9, 11],\n",
    "    'model__weights': ['uniform', 'distance']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 201 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 207 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 198 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 196 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n",
      "/Users/sergio/PycharmProjects/MLEARN/venv/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "ValueError: With under-sampling methods, the number of samples in a class should be less or equal to the original number of samples. Originally, there is 197 samples and 300 samples are asked.\n",
      "\n",
      "  FitFailedWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=5, random_state=0, test_size=0.15, train_size=None),\n",
       "             error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('features',\n",
       "                                        ColumnTransformer(n_jobs=None,\n",
       "                                                          remainder='drop',\n",
       "                                                          sparse_threshold=0.3,\n",
       "                                                          transformer_weights=None,\n",
       "                                                          transformers=[('roll',\n",
       "                                                                         Pipeline(memory=None,\n",
       "                                                                                  steps=[('meantransform',\n",
       "                                                                                          MeanTransform())],\n",
       "                                                                                  verbose=False),\n",
       "                                                                         slic...\n",
       "                                       RandomUnderSampler(random_state=1,\n",
       "                                                          replacement=True,\n",
       "                                                          sampling_strategy={0: 300,\n",
       "                                                                             1: 300,\n",
       "                                                                             2: 300,\n",
       "                                                                             3: 300,\n",
       "                                                                             4: 300,\n",
       "                                                                             5: 300})],\n",
       "                         'model__n_neighbors': [3, 5, 7, 9, 11],\n",
       "                         'model__weights': ['uniform', 'distance'],\n",
       "                         'normalize': ['passthrough',\n",
       "                                       StandardScaler(copy=True, with_mean=True,\n",
       "                                                      with_std=True)]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(clf, param_grid, cv=cv)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'balancing': 'passthrough',\n",
       " 'model__n_neighbors': 3,\n",
       " 'model__weights': 'distance',\n",
       " 'normalize': 'passthrough'}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9529126213592234"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results = pd.DataFrame(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_balancing</th>\n",
       "      <th>param_model__n_neighbors</th>\n",
       "      <th>param_model__weights</th>\n",
       "      <th>param_normalize</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.126054</td>\n",
       "      <td>0.018511</td>\n",
       "      <td>0.069158</td>\n",
       "      <td>0.006795</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.966019</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.963592</td>\n",
       "      <td>0.924757</td>\n",
       "      <td>0.950971</td>\n",
       "      <td>0.014836</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.115874</td>\n",
       "      <td>0.010991</td>\n",
       "      <td>0.174977</td>\n",
       "      <td>0.010494</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.910194</td>\n",
       "      <td>0.915049</td>\n",
       "      <td>0.893204</td>\n",
       "      <td>0.913107</td>\n",
       "      <td>0.013152</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.119932</td>\n",
       "      <td>0.015438</td>\n",
       "      <td>0.037982</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.966019</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.952913</td>\n",
       "      <td>0.012898</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.121935</td>\n",
       "      <td>0.011731</td>\n",
       "      <td>0.151734</td>\n",
       "      <td>0.008182</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.907767</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.922330</td>\n",
       "      <td>0.910194</td>\n",
       "      <td>0.893204</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.012565</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.118378</td>\n",
       "      <td>0.010926</td>\n",
       "      <td>0.072287</td>\n",
       "      <td>0.004998</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.922330</td>\n",
       "      <td>0.945631</td>\n",
       "      <td>0.013610</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.107552</td>\n",
       "      <td>0.001861</td>\n",
       "      <td>0.170167</td>\n",
       "      <td>0.011516</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.895631</td>\n",
       "      <td>0.917476</td>\n",
       "      <td>0.893204</td>\n",
       "      <td>0.905340</td>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.899029</td>\n",
       "      <td>0.011549</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.100535</td>\n",
       "      <td>0.002356</td>\n",
       "      <td>0.035135</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.963592</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.011950</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.115392</td>\n",
       "      <td>0.007671</td>\n",
       "      <td>0.159464</td>\n",
       "      <td>0.020134</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.890777</td>\n",
       "      <td>0.915049</td>\n",
       "      <td>0.898058</td>\n",
       "      <td>0.907767</td>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.899029</td>\n",
       "      <td>0.011343</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.106099</td>\n",
       "      <td>0.001488</td>\n",
       "      <td>0.071647</td>\n",
       "      <td>0.004946</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.915049</td>\n",
       "      <td>0.937379</td>\n",
       "      <td>0.015760</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.117611</td>\n",
       "      <td>0.019274</td>\n",
       "      <td>0.173070</td>\n",
       "      <td>0.009351</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.876214</td>\n",
       "      <td>0.910194</td>\n",
       "      <td>0.866505</td>\n",
       "      <td>0.888350</td>\n",
       "      <td>0.861650</td>\n",
       "      <td>0.880583</td>\n",
       "      <td>0.017395</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.106791</td>\n",
       "      <td>0.001351</td>\n",
       "      <td>0.036767</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.945146</td>\n",
       "      <td>0.013696</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.106227</td>\n",
       "      <td>0.007607</td>\n",
       "      <td>0.149419</td>\n",
       "      <td>0.018466</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.885922</td>\n",
       "      <td>0.907767</td>\n",
       "      <td>0.873786</td>\n",
       "      <td>0.885922</td>\n",
       "      <td>0.851942</td>\n",
       "      <td>0.881068</td>\n",
       "      <td>0.018228</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.099407</td>\n",
       "      <td>0.002790</td>\n",
       "      <td>0.063726</td>\n",
       "      <td>0.002107</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.937864</td>\n",
       "      <td>0.014203</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.100615</td>\n",
       "      <td>0.002705</td>\n",
       "      <td>0.161829</td>\n",
       "      <td>0.002260</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.871359</td>\n",
       "      <td>0.898058</td>\n",
       "      <td>0.847087</td>\n",
       "      <td>0.873786</td>\n",
       "      <td>0.856796</td>\n",
       "      <td>0.869417</td>\n",
       "      <td>0.017327</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.098939</td>\n",
       "      <td>0.002453</td>\n",
       "      <td>0.034354</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.919903</td>\n",
       "      <td>0.943204</td>\n",
       "      <td>0.015320</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.103152</td>\n",
       "      <td>0.005932</td>\n",
       "      <td>0.143948</td>\n",
       "      <td>0.001404</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.866505</td>\n",
       "      <td>0.900485</td>\n",
       "      <td>0.859223</td>\n",
       "      <td>0.873786</td>\n",
       "      <td>0.847087</td>\n",
       "      <td>0.869417</td>\n",
       "      <td>0.017862</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.099213</td>\n",
       "      <td>0.003950</td>\n",
       "      <td>0.059911</td>\n",
       "      <td>0.002203</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.907767</td>\n",
       "      <td>0.930097</td>\n",
       "      <td>0.012509</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.099761</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.160832</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.851942</td>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.849515</td>\n",
       "      <td>0.866505</td>\n",
       "      <td>0.839806</td>\n",
       "      <td>0.858252</td>\n",
       "      <td>0.015243</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.101059</td>\n",
       "      <td>0.002544</td>\n",
       "      <td>0.037390</td>\n",
       "      <td>0.001925</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.938835</td>\n",
       "      <td>0.014836</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.103483</td>\n",
       "      <td>0.002726</td>\n",
       "      <td>0.143005</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': 'passthrough', 'model__n_neighbo...</td>\n",
       "      <td>0.849515</td>\n",
       "      <td>0.883495</td>\n",
       "      <td>0.847087</td>\n",
       "      <td>0.873786</td>\n",
       "      <td>0.837379</td>\n",
       "      <td>0.858252</td>\n",
       "      <td>0.017408</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.160877</td>\n",
       "      <td>0.008547</td>\n",
       "      <td>0.057137</td>\n",
       "      <td>0.002358</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.966019</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.950485</td>\n",
       "      <td>0.012146</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.165704</td>\n",
       "      <td>0.009603</td>\n",
       "      <td>0.166447</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.950485</td>\n",
       "      <td>0.006254</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.157697</td>\n",
       "      <td>0.009830</td>\n",
       "      <td>0.036138</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.966019</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.936893</td>\n",
       "      <td>0.952427</td>\n",
       "      <td>0.010366</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.165779</td>\n",
       "      <td>0.009679</td>\n",
       "      <td>0.146145</td>\n",
       "      <td>0.007421</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.951942</td>\n",
       "      <td>0.006585</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.157763</td>\n",
       "      <td>0.007847</td>\n",
       "      <td>0.058285</td>\n",
       "      <td>0.003570</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.924757</td>\n",
       "      <td>0.945146</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.163816</td>\n",
       "      <td>0.011319</td>\n",
       "      <td>0.168224</td>\n",
       "      <td>0.002054</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.944660</td>\n",
       "      <td>0.008464</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.157020</td>\n",
       "      <td>0.008688</td>\n",
       "      <td>0.035670</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.948544</td>\n",
       "      <td>0.009133</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.166275</td>\n",
       "      <td>0.007757</td>\n",
       "      <td>0.145810</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.947573</td>\n",
       "      <td>0.007457</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.159559</td>\n",
       "      <td>0.010217</td>\n",
       "      <td>0.058537</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.943204</td>\n",
       "      <td>0.010702</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.163249</td>\n",
       "      <td>0.010313</td>\n",
       "      <td>0.170888</td>\n",
       "      <td>0.003592</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.922330</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.011791</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.158731</td>\n",
       "      <td>0.011844</td>\n",
       "      <td>0.040310</td>\n",
       "      <td>0.002345</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.008818</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.163752</td>\n",
       "      <td>0.010414</td>\n",
       "      <td>0.148418</td>\n",
       "      <td>0.002356</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.924757</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.941262</td>\n",
       "      <td>0.009388</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.161260</td>\n",
       "      <td>0.012683</td>\n",
       "      <td>0.060363</td>\n",
       "      <td>0.002398</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.919903</td>\n",
       "      <td>0.942233</td>\n",
       "      <td>0.013505</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.164099</td>\n",
       "      <td>0.011752</td>\n",
       "      <td>0.173164</td>\n",
       "      <td>0.004389</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.936408</td>\n",
       "      <td>0.006933</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.166784</td>\n",
       "      <td>0.008636</td>\n",
       "      <td>0.041053</td>\n",
       "      <td>0.004078</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.936893</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.956311</td>\n",
       "      <td>0.924757</td>\n",
       "      <td>0.944660</td>\n",
       "      <td>0.012223</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.166786</td>\n",
       "      <td>0.010754</td>\n",
       "      <td>0.153204</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.936893</td>\n",
       "      <td>0.949029</td>\n",
       "      <td>0.936893</td>\n",
       "      <td>0.941748</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.160951</td>\n",
       "      <td>0.009800</td>\n",
       "      <td>0.061655</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.932039</td>\n",
       "      <td>0.953883</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.936893</td>\n",
       "      <td>0.912621</td>\n",
       "      <td>0.935922</td>\n",
       "      <td>0.013782</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.167661</td>\n",
       "      <td>0.012151</td>\n",
       "      <td>0.172737</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.934466</td>\n",
       "      <td>0.936893</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.934951</td>\n",
       "      <td>0.006761</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.162863</td>\n",
       "      <td>0.011485</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.003116</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.961165</td>\n",
       "      <td>0.951456</td>\n",
       "      <td>0.958738</td>\n",
       "      <td>0.922330</td>\n",
       "      <td>0.944660</td>\n",
       "      <td>0.015760</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.170555</td>\n",
       "      <td>0.011091</td>\n",
       "      <td>0.153533</td>\n",
       "      <td>0.003708</td>\n",
       "      <td>SMOTE(k_neighbors=5, n_jobs=None, random_state...</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': SMOTE(k_neighbors=5, n_jobs=None...</td>\n",
       "      <td>0.929612</td>\n",
       "      <td>0.946602</td>\n",
       "      <td>0.939320</td>\n",
       "      <td>0.944175</td>\n",
       "      <td>0.924757</td>\n",
       "      <td>0.936893</td>\n",
       "      <td>0.008408</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.091456</td>\n",
       "      <td>0.005909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.094691</td>\n",
       "      <td>0.005839</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>3</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.092967</td>\n",
       "      <td>0.006295</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.103653</td>\n",
       "      <td>0.012077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>3</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.109626</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.113283</td>\n",
       "      <td>0.009506</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>5</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.105445</td>\n",
       "      <td>0.010598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.106672</td>\n",
       "      <td>0.012400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>5</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.094744</td>\n",
       "      <td>0.003168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.096983</td>\n",
       "      <td>0.004376</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>7</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.093599</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.095704</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>7</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.094419</td>\n",
       "      <td>0.003395</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.096736</td>\n",
       "      <td>0.003912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>9</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.096860</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.097342</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>9</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.096022</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.099929</td>\n",
       "      <td>0.003334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>11</td>\n",
       "      <td>uniform</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.097713</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>passthrough</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.100508</td>\n",
       "      <td>0.005953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>RandomUnderSampler(random_state=1, replacement...</td>\n",
       "      <td>11</td>\n",
       "      <td>distance</td>\n",
       "      <td>StandardScaler(copy=True, with_mean=True, with...</td>\n",
       "      <td>{'balancing': RandomUnderSampler(random_state=...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.126054      0.018511         0.069158        0.006795   \n",
       "1        0.115874      0.010991         0.174977        0.010494   \n",
       "2        0.119932      0.015438         0.037982        0.002715   \n",
       "3        0.121935      0.011731         0.151734        0.008182   \n",
       "4        0.118378      0.010926         0.072287        0.004998   \n",
       "5        0.107552      0.001861         0.170167        0.011516   \n",
       "6        0.100535      0.002356         0.035135        0.001765   \n",
       "7        0.115392      0.007671         0.159464        0.020134   \n",
       "8        0.106099      0.001488         0.071647        0.004946   \n",
       "9        0.117611      0.019274         0.173070        0.009351   \n",
       "10       0.106791      0.001351         0.036767        0.000359   \n",
       "11       0.106227      0.007607         0.149419        0.018466   \n",
       "12       0.099407      0.002790         0.063726        0.002107   \n",
       "13       0.100615      0.002705         0.161829        0.002260   \n",
       "14       0.098939      0.002453         0.034354        0.001822   \n",
       "15       0.103152      0.005932         0.143948        0.001404   \n",
       "16       0.099213      0.003950         0.059911        0.002203   \n",
       "17       0.099761      0.003210         0.160832        0.003327   \n",
       "18       0.101059      0.002544         0.037390        0.001925   \n",
       "19       0.103483      0.002726         0.143005        0.004737   \n",
       "20       0.160877      0.008547         0.057137        0.002358   \n",
       "21       0.165704      0.009603         0.166447        0.003160   \n",
       "22       0.157697      0.009830         0.036138        0.001817   \n",
       "23       0.165779      0.009679         0.146145        0.007421   \n",
       "24       0.157763      0.007847         0.058285        0.003570   \n",
       "25       0.163816      0.011319         0.168224        0.002054   \n",
       "26       0.157020      0.008688         0.035670        0.001127   \n",
       "27       0.166275      0.007757         0.145810        0.003983   \n",
       "28       0.159559      0.010217         0.058537        0.001709   \n",
       "29       0.163249      0.010313         0.170888        0.003592   \n",
       "30       0.158731      0.011844         0.040310        0.002345   \n",
       "31       0.163752      0.010414         0.148418        0.002356   \n",
       "32       0.161260      0.012683         0.060363        0.002398   \n",
       "33       0.164099      0.011752         0.173164        0.004389   \n",
       "34       0.166784      0.008636         0.041053        0.004078   \n",
       "35       0.166786      0.010754         0.153204        0.001394   \n",
       "36       0.160951      0.009800         0.061655        0.002261   \n",
       "37       0.167661      0.012151         0.172737        0.003261   \n",
       "38       0.162863      0.011485         0.042062        0.003116   \n",
       "39       0.170555      0.011091         0.153533        0.003708   \n",
       "40       0.091456      0.005909         0.000000        0.000000   \n",
       "41       0.094691      0.005839         0.000000        0.000000   \n",
       "42       0.092967      0.006295         0.000000        0.000000   \n",
       "43       0.103653      0.012077         0.000000        0.000000   \n",
       "44       0.109626      0.006193         0.000000        0.000000   \n",
       "45       0.113283      0.009506         0.000000        0.000000   \n",
       "46       0.105445      0.010598         0.000000        0.000000   \n",
       "47       0.106672      0.012400         0.000000        0.000000   \n",
       "48       0.094744      0.003168         0.000000        0.000000   \n",
       "49       0.096983      0.004376         0.000000        0.000000   \n",
       "50       0.093599      0.002551         0.000000        0.000000   \n",
       "51       0.095704      0.002648         0.000000        0.000000   \n",
       "52       0.094419      0.003395         0.000000        0.000000   \n",
       "53       0.096736      0.003912         0.000000        0.000000   \n",
       "54       0.096860      0.003187         0.000000        0.000000   \n",
       "55       0.097342      0.001177         0.000000        0.000000   \n",
       "56       0.096022      0.001029         0.000000        0.000000   \n",
       "57       0.099929      0.003334         0.000000        0.000000   \n",
       "58       0.097713      0.000396         0.000000        0.000000   \n",
       "59       0.100508      0.005953         0.000000        0.000000   \n",
       "\n",
       "                                      param_balancing  \\\n",
       "0                                         passthrough   \n",
       "1                                         passthrough   \n",
       "2                                         passthrough   \n",
       "3                                         passthrough   \n",
       "4                                         passthrough   \n",
       "5                                         passthrough   \n",
       "6                                         passthrough   \n",
       "7                                         passthrough   \n",
       "8                                         passthrough   \n",
       "9                                         passthrough   \n",
       "10                                        passthrough   \n",
       "11                                        passthrough   \n",
       "12                                        passthrough   \n",
       "13                                        passthrough   \n",
       "14                                        passthrough   \n",
       "15                                        passthrough   \n",
       "16                                        passthrough   \n",
       "17                                        passthrough   \n",
       "18                                        passthrough   \n",
       "19                                        passthrough   \n",
       "20  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "21  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "22  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "23  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "24  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "25  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "26  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "27  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "28  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "29  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "30  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "31  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "32  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "33  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "34  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "35  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "36  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "37  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "38  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "39  SMOTE(k_neighbors=5, n_jobs=None, random_state...   \n",
       "40  RandomUnderSampler(random_state=1, replacement...   \n",
       "41  RandomUnderSampler(random_state=1, replacement...   \n",
       "42  RandomUnderSampler(random_state=1, replacement...   \n",
       "43  RandomUnderSampler(random_state=1, replacement...   \n",
       "44  RandomUnderSampler(random_state=1, replacement...   \n",
       "45  RandomUnderSampler(random_state=1, replacement...   \n",
       "46  RandomUnderSampler(random_state=1, replacement...   \n",
       "47  RandomUnderSampler(random_state=1, replacement...   \n",
       "48  RandomUnderSampler(random_state=1, replacement...   \n",
       "49  RandomUnderSampler(random_state=1, replacement...   \n",
       "50  RandomUnderSampler(random_state=1, replacement...   \n",
       "51  RandomUnderSampler(random_state=1, replacement...   \n",
       "52  RandomUnderSampler(random_state=1, replacement...   \n",
       "53  RandomUnderSampler(random_state=1, replacement...   \n",
       "54  RandomUnderSampler(random_state=1, replacement...   \n",
       "55  RandomUnderSampler(random_state=1, replacement...   \n",
       "56  RandomUnderSampler(random_state=1, replacement...   \n",
       "57  RandomUnderSampler(random_state=1, replacement...   \n",
       "58  RandomUnderSampler(random_state=1, replacement...   \n",
       "59  RandomUnderSampler(random_state=1, replacement...   \n",
       "\n",
       "   param_model__n_neighbors param_model__weights  \\\n",
       "0                         3              uniform   \n",
       "1                         3              uniform   \n",
       "2                         3             distance   \n",
       "3                         3             distance   \n",
       "4                         5              uniform   \n",
       "5                         5              uniform   \n",
       "6                         5             distance   \n",
       "7                         5             distance   \n",
       "8                         7              uniform   \n",
       "9                         7              uniform   \n",
       "10                        7             distance   \n",
       "11                        7             distance   \n",
       "12                        9              uniform   \n",
       "13                        9              uniform   \n",
       "14                        9             distance   \n",
       "15                        9             distance   \n",
       "16                       11              uniform   \n",
       "17                       11              uniform   \n",
       "18                       11             distance   \n",
       "19                       11             distance   \n",
       "20                        3              uniform   \n",
       "21                        3              uniform   \n",
       "22                        3             distance   \n",
       "23                        3             distance   \n",
       "24                        5              uniform   \n",
       "25                        5              uniform   \n",
       "26                        5             distance   \n",
       "27                        5             distance   \n",
       "28                        7              uniform   \n",
       "29                        7              uniform   \n",
       "30                        7             distance   \n",
       "31                        7             distance   \n",
       "32                        9              uniform   \n",
       "33                        9              uniform   \n",
       "34                        9             distance   \n",
       "35                        9             distance   \n",
       "36                       11              uniform   \n",
       "37                       11              uniform   \n",
       "38                       11             distance   \n",
       "39                       11             distance   \n",
       "40                        3              uniform   \n",
       "41                        3              uniform   \n",
       "42                        3             distance   \n",
       "43                        3             distance   \n",
       "44                        5              uniform   \n",
       "45                        5              uniform   \n",
       "46                        5             distance   \n",
       "47                        5             distance   \n",
       "48                        7              uniform   \n",
       "49                        7              uniform   \n",
       "50                        7             distance   \n",
       "51                        7             distance   \n",
       "52                        9              uniform   \n",
       "53                        9              uniform   \n",
       "54                        9             distance   \n",
       "55                        9             distance   \n",
       "56                       11              uniform   \n",
       "57                       11              uniform   \n",
       "58                       11             distance   \n",
       "59                       11             distance   \n",
       "\n",
       "                                      param_normalize  \\\n",
       "0                                         passthrough   \n",
       "1   StandardScaler(copy=True, with_mean=True, with...   \n",
       "2                                         passthrough   \n",
       "3   StandardScaler(copy=True, with_mean=True, with...   \n",
       "4                                         passthrough   \n",
       "5   StandardScaler(copy=True, with_mean=True, with...   \n",
       "6                                         passthrough   \n",
       "7   StandardScaler(copy=True, with_mean=True, with...   \n",
       "8                                         passthrough   \n",
       "9   StandardScaler(copy=True, with_mean=True, with...   \n",
       "10                                        passthrough   \n",
       "11  StandardScaler(copy=True, with_mean=True, with...   \n",
       "12                                        passthrough   \n",
       "13  StandardScaler(copy=True, with_mean=True, with...   \n",
       "14                                        passthrough   \n",
       "15  StandardScaler(copy=True, with_mean=True, with...   \n",
       "16                                        passthrough   \n",
       "17  StandardScaler(copy=True, with_mean=True, with...   \n",
       "18                                        passthrough   \n",
       "19  StandardScaler(copy=True, with_mean=True, with...   \n",
       "20                                        passthrough   \n",
       "21  StandardScaler(copy=True, with_mean=True, with...   \n",
       "22                                        passthrough   \n",
       "23  StandardScaler(copy=True, with_mean=True, with...   \n",
       "24                                        passthrough   \n",
       "25  StandardScaler(copy=True, with_mean=True, with...   \n",
       "26                                        passthrough   \n",
       "27  StandardScaler(copy=True, with_mean=True, with...   \n",
       "28                                        passthrough   \n",
       "29  StandardScaler(copy=True, with_mean=True, with...   \n",
       "30                                        passthrough   \n",
       "31  StandardScaler(copy=True, with_mean=True, with...   \n",
       "32                                        passthrough   \n",
       "33  StandardScaler(copy=True, with_mean=True, with...   \n",
       "34                                        passthrough   \n",
       "35  StandardScaler(copy=True, with_mean=True, with...   \n",
       "36                                        passthrough   \n",
       "37  StandardScaler(copy=True, with_mean=True, with...   \n",
       "38                                        passthrough   \n",
       "39  StandardScaler(copy=True, with_mean=True, with...   \n",
       "40                                        passthrough   \n",
       "41  StandardScaler(copy=True, with_mean=True, with...   \n",
       "42                                        passthrough   \n",
       "43  StandardScaler(copy=True, with_mean=True, with...   \n",
       "44                                        passthrough   \n",
       "45  StandardScaler(copy=True, with_mean=True, with...   \n",
       "46                                        passthrough   \n",
       "47  StandardScaler(copy=True, with_mean=True, with...   \n",
       "48                                        passthrough   \n",
       "49  StandardScaler(copy=True, with_mean=True, with...   \n",
       "50                                        passthrough   \n",
       "51  StandardScaler(copy=True, with_mean=True, with...   \n",
       "52                                        passthrough   \n",
       "53  StandardScaler(copy=True, with_mean=True, with...   \n",
       "54                                        passthrough   \n",
       "55  StandardScaler(copy=True, with_mean=True, with...   \n",
       "56                                        passthrough   \n",
       "57  StandardScaler(copy=True, with_mean=True, with...   \n",
       "58                                        passthrough   \n",
       "59  StandardScaler(copy=True, with_mean=True, with...   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'balancing': 'passthrough', 'model__n_neighbo...           0.946602   \n",
       "1   {'balancing': 'passthrough', 'model__n_neighbo...           0.912621   \n",
       "2   {'balancing': 'passthrough', 'model__n_neighbo...           0.949029   \n",
       "3   {'balancing': 'passthrough', 'model__n_neighbo...           0.907767   \n",
       "4   {'balancing': 'passthrough', 'model__n_neighbo...           0.939320   \n",
       "5   {'balancing': 'passthrough', 'model__n_neighbo...           0.895631   \n",
       "6   {'balancing': 'passthrough', 'model__n_neighbo...           0.944175   \n",
       "7   {'balancing': 'passthrough', 'model__n_neighbo...           0.890777   \n",
       "8   {'balancing': 'passthrough', 'model__n_neighbo...           0.927184   \n",
       "9   {'balancing': 'passthrough', 'model__n_neighbo...           0.876214   \n",
       "10  {'balancing': 'passthrough', 'model__n_neighbo...           0.932039   \n",
       "11  {'balancing': 'passthrough', 'model__n_neighbo...           0.885922   \n",
       "12  {'balancing': 'passthrough', 'model__n_neighbo...           0.932039   \n",
       "13  {'balancing': 'passthrough', 'model__n_neighbo...           0.871359   \n",
       "14  {'balancing': 'passthrough', 'model__n_neighbo...           0.932039   \n",
       "15  {'balancing': 'passthrough', 'model__n_neighbo...           0.866505   \n",
       "16  {'balancing': 'passthrough', 'model__n_neighbo...           0.932039   \n",
       "17  {'balancing': 'passthrough', 'model__n_neighbo...           0.851942   \n",
       "18  {'balancing': 'passthrough', 'model__n_neighbo...           0.934466   \n",
       "19  {'balancing': 'passthrough', 'model__n_neighbo...           0.849515   \n",
       "20  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.944175   \n",
       "21  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.941748   \n",
       "22  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.946602   \n",
       "23  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.941748   \n",
       "24  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.939320   \n",
       "25  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.932039   \n",
       "26  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.941748   \n",
       "27  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.939320   \n",
       "28  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.934466   \n",
       "29  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.922330   \n",
       "30  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.941748   \n",
       "31  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.924757   \n",
       "32  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.934466   \n",
       "33  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.932039   \n",
       "34  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.936893   \n",
       "35  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.936893   \n",
       "36  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.932039   \n",
       "37  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.934466   \n",
       "38  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.929612   \n",
       "39  {'balancing': SMOTE(k_neighbors=5, n_jobs=None...           0.929612   \n",
       "40  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "41  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "42  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "43  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "44  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "45  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "46  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "47  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "48  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "49  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "50  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "51  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "52  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "53  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "54  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "55  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "56  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "57  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "58  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "59  {'balancing': RandomUnderSampler(random_state=...                NaN   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.966019           0.953883           0.963592   \n",
       "1            0.934466           0.910194           0.915049   \n",
       "2            0.966019           0.958738           0.961165   \n",
       "3            0.929612           0.922330           0.910194   \n",
       "4            0.961165           0.951456           0.953883   \n",
       "5            0.917476           0.893204           0.905340   \n",
       "6            0.963592           0.956311           0.956311   \n",
       "7            0.915049           0.898058           0.907767   \n",
       "8            0.953883           0.934466           0.956311   \n",
       "9            0.910194           0.866505           0.888350   \n",
       "10           0.961165           0.946602           0.958738   \n",
       "11           0.907767           0.873786           0.885922   \n",
       "12           0.951456           0.946602           0.946602   \n",
       "13           0.898058           0.847087           0.873786   \n",
       "14           0.961165           0.946602           0.956311   \n",
       "15           0.900485           0.859223           0.873786   \n",
       "16           0.946602           0.932039           0.932039   \n",
       "17           0.883495           0.849515           0.866505   \n",
       "18           0.953883           0.941748           0.951456   \n",
       "19           0.883495           0.847087           0.873786   \n",
       "20           0.966019           0.949029           0.961165   \n",
       "21           0.951456           0.949029           0.961165   \n",
       "22           0.966019           0.951456           0.961165   \n",
       "23           0.956311           0.958738           0.956311   \n",
       "24           0.958738           0.949029           0.953883   \n",
       "25           0.953883           0.939320           0.953883   \n",
       "26           0.958738           0.951456           0.956311   \n",
       "27           0.953883           0.944175           0.958738   \n",
       "28           0.953883           0.946602           0.953883   \n",
       "29           0.946602           0.941748           0.958738   \n",
       "30           0.956311           0.949029           0.953883   \n",
       "31           0.951456           0.941748           0.949029   \n",
       "32           0.953883           0.946602           0.956311   \n",
       "33           0.941748           0.927184           0.946602   \n",
       "34           0.956311           0.949029           0.956311   \n",
       "35           0.949029           0.936893           0.941748   \n",
       "36           0.953883           0.944175           0.936893   \n",
       "37           0.936893           0.929612           0.946602   \n",
       "38           0.961165           0.951456           0.958738   \n",
       "39           0.946602           0.939320           0.944175   \n",
       "40                NaN                NaN                NaN   \n",
       "41                NaN                NaN                NaN   \n",
       "42                NaN                NaN                NaN   \n",
       "43                NaN                NaN                NaN   \n",
       "44                NaN                NaN                NaN   \n",
       "45                NaN                NaN                NaN   \n",
       "46                NaN                NaN                NaN   \n",
       "47                NaN                NaN                NaN   \n",
       "48                NaN                NaN                NaN   \n",
       "49                NaN                NaN                NaN   \n",
       "50                NaN                NaN                NaN   \n",
       "51                NaN                NaN                NaN   \n",
       "52                NaN                NaN                NaN   \n",
       "53                NaN                NaN                NaN   \n",
       "54                NaN                NaN                NaN   \n",
       "55                NaN                NaN                NaN   \n",
       "56                NaN                NaN                NaN   \n",
       "57                NaN                NaN                NaN   \n",
       "58                NaN                NaN                NaN   \n",
       "59                NaN                NaN                NaN   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.924757         0.950971        0.014836                4  \n",
       "1            0.893204         0.913107        0.013152               31  \n",
       "2            0.929612         0.952913        0.012898                1  \n",
       "3            0.893204         0.912621        0.012565               32  \n",
       "4            0.922330         0.945631        0.013610               11  \n",
       "5            0.883495         0.899029        0.011549               33  \n",
       "6            0.929612         0.950000        0.011950                7  \n",
       "7            0.883495         0.899029        0.011343               33  \n",
       "8            0.915049         0.937379        0.015760               25  \n",
       "9            0.861650         0.880583        0.017395               36  \n",
       "10           0.927184         0.945146        0.013696               12  \n",
       "11           0.851942         0.881068        0.018228               35  \n",
       "12           0.912621         0.937864        0.014203               24  \n",
       "13           0.856796         0.869417        0.017327               37  \n",
       "14           0.919903         0.943204        0.015320               17  \n",
       "15           0.847087         0.869417        0.017862               37  \n",
       "16           0.907767         0.930097        0.012509               30  \n",
       "17           0.839806         0.858252        0.015243               39  \n",
       "18           0.912621         0.938835        0.014836               23  \n",
       "19           0.837379         0.858252        0.017408               39  \n",
       "20           0.932039         0.950485        0.012146                5  \n",
       "21           0.949029         0.950485        0.006254                6  \n",
       "22           0.936893         0.952427        0.010366                2  \n",
       "23           0.946602         0.951942        0.006585                3  \n",
       "24           0.924757         0.945146        0.012048               12  \n",
       "25           0.944175         0.944660        0.008464               14  \n",
       "26           0.934466         0.948544        0.009133                8  \n",
       "27           0.941748         0.947573        0.007457                9  \n",
       "28           0.927184         0.943204        0.010702               18  \n",
       "29           0.939320         0.941748        0.011791               20  \n",
       "30           0.932039         0.946602        0.008818               10  \n",
       "31           0.939320         0.941262        0.009388               21  \n",
       "32           0.919903         0.942233        0.013505               19  \n",
       "33           0.934466         0.936408        0.006933               27  \n",
       "34           0.924757         0.944660        0.012223               14  \n",
       "35           0.932039         0.939320        0.005744               22  \n",
       "36           0.912621         0.935922        0.013782               28  \n",
       "37           0.927184         0.934951        0.006761               29  \n",
       "38           0.922330         0.944660        0.015760               14  \n",
       "39           0.924757         0.936893        0.008408               26  \n",
       "40                NaN              NaN             NaN               57  \n",
       "41                NaN              NaN             NaN               56  \n",
       "42                NaN              NaN             NaN               50  \n",
       "43                NaN              NaN             NaN               55  \n",
       "44                NaN              NaN             NaN               54  \n",
       "45                NaN              NaN             NaN               53  \n",
       "46                NaN              NaN             NaN               52  \n",
       "47                NaN              NaN             NaN               51  \n",
       "48                NaN              NaN             NaN               59  \n",
       "49                NaN              NaN             NaN               49  \n",
       "50                NaN              NaN             NaN               41  \n",
       "51                NaN              NaN             NaN               47  \n",
       "52                NaN              NaN             NaN               46  \n",
       "53                NaN              NaN             NaN               45  \n",
       "54                NaN              NaN             NaN               44  \n",
       "55                NaN              NaN             NaN               43  \n",
       "56                NaN              NaN             NaN               42  \n",
       "57                NaN              NaN             NaN               48  \n",
       "58                NaN              NaN             NaN               58  \n",
       "59                NaN              NaN             NaN               60  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dws       1.00      1.00      1.00       236\n",
      "         jog       1.00      1.00      1.00       265\n",
      "         sit       1.00      1.00      1.00       663\n",
      "         std       1.00      1.00      1.00       615\n",
      "         ups       1.00      1.00      1.00       298\n",
      "         wlk       1.00      1.00      1.00       666\n",
      "\n",
      "    accuracy                           1.00      2743\n",
      "   macro avg       1.00      1.00      1.00      2743\n",
      "weighted avg       1.00      1.00      1.00      2743\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, grid_search.predict(X_train), target_names=label_encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x11769e850>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(grid_search, X_train, y_train,\n",
    "                      display_labels=label_encoder.classes_,\n",
    "                      cmap=plt.cm.Blues,\n",
    "                      normalize=\"true\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         dws       0.92      0.88      0.90       123\n",
      "         jog       0.99      0.98      0.99       119\n",
      "         sit       0.97      0.93      0.95       341\n",
      "         std       0.92      0.96      0.94       285\n",
      "         ups       0.91      0.91      0.91       138\n",
      "         wlk       0.96      0.98      0.97       346\n",
      "\n",
      "    accuracy                           0.95      1352\n",
      "   macro avg       0.95      0.94      0.94      1352\n",
      "weighted avg       0.95      0.95      0.95      1352\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_search.predict(X_test), target_names=label_encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1390d8510>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(grid_search, X_test, y_test,\n",
    "                      display_labels=label_encoder.classes_,\n",
    "                      cmap=plt.cm.Blues,\n",
    "                      normalize=\"true\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}