PYCARET_FEMALECLASSIFIER.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Users/daniel.clark/.pyenv/versions/3.7.5/lib/python3.7/site-packages/pandas/compat/__init__.py:120: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n",
      "/Users/daniel.clark/.pyenv/versions/3.7.5/lib/python3.7/site-packages/ipykernel_launcher.py:27: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "from io import StringIO\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "#import modin.pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#import cufflinks as cf\n",
    "#cf.go_offline()\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import svm\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "#from pathos.multiprocessing import ProcessingPool as Pool\n",
    "import pandas.util.testing as tm\n",
    "import multiprocessing as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "femaledrive = 'https://drive.google.com/file/d/1tWrKX1Q-iRcmrWUMbOYd4IUBoPPmFs_U/view?usp=sharing'\n",
    "\n",
    "file_id = femaledrive.split('/')[-2]\n",
    "dwn_url='https://drive.google.com/uc?export=download&id=' + file_id\n",
    "url = requests.get(dwn_url).text\n",
    "csv_raw = StringIO(url)\n",
    "femaleclean = pd.read_csv(csv_raw)\n",
    "femaleclean.head()\n",
    "femaleclean = femaleclean.iloc[0:190047]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "femaleclean = femaleclean[(femaleclean.OBSERVED == 1)]\n",
    "femaleclean = femaleclean[(femaleclean.Sex == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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   "outputs": [
    {
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      "dtypes: float64(52), int64(18)\n",
      "memory usage: 1.1 MB\n"
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   "source": [
    "femaleclean.info()"
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  {
   "cell_type": "code",
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    "femaleclean['time_difference'] = femaleclean['datetime'] - femaleclean['datetime'].shift(1)\n",
    "\n",
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   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "femaleclean['time_difference'] = femaleclean['time_difference'].fillna(0)\n"
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   "cell_type": "code",
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       "      <td>4</td>\n",
       "      <td>1.732025</td>\n",
       "      <td>0.41597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "      <td>50.005390</td>\n",
       "      <td>363.594567</td>\n",
       "      <td>164.429215</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-151.907745</td>\n",
       "      <td>-234.307495</td>\n",
       "      <td>-60.000000</td>\n",
       "      <td>-169.705627</td>\n",
       "      <td>-60.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.726947</td>\n",
       "      <td>0.54167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  FID  Id  Sex  Bear_ID  SAMPLEID   TURNANGLE  STEPLENGTH  \\\n",
       "0           1    0   0    0        7         0  122.738490  329.371060   \n",
       "1          12   11   0    0        7        11 -163.857763  596.801120   \n",
       "2          23   22   0    0        7        22 -150.260818  554.762369   \n",
       "3          34   33   0    0        7        33   96.365383  617.323955   \n",
       "4          45   44   0    0        7        44   50.005390  363.594567   \n",
       "\n",
       "      BEARING  OBSERVED  ...  naturalBEG  naturalEND   NatnegLWM   NatnegMIN  \\\n",
       "0  332.177023         1  ...           1           1 -256.918925 -400.249908   \n",
       "1  168.319259         1  ...           1           1 -333.297213 -496.588348   \n",
       "2   18.058442         1  ...           1           1 -419.938047 -550.726807   \n",
       "3  114.423824         1  ...           1           1 -306.522643 -417.851654   \n",
       "4  164.429215         1  ...           1           1 -151.907745 -234.307495   \n",
       "\n",
       "    NatnegMAX   NatnegBEG   NatnegEND  STRATUM     resid  time_difference  \n",
       "0 -108.166542 -400.249908 -108.166542        1  1.619778          0.00000  \n",
       "1 -108.166542 -108.166542 -416.773315        2  1.887523          0.29166  \n",
       "2 -258.069763 -416.773315 -258.069763        3  1.712916          0.16667  \n",
       "3 -169.705627 -258.069763 -169.705627        4  1.732025          0.41597  \n",
       "4  -60.000000 -169.705627  -60.000000        5  1.726947          0.54167  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "femaleclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "femaleclean['stepsbytime'] = femaleclean['STEPLENGTH'] / femaleclean['time_difference']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2222.173295977749"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#maleclean['stepsbytime'].mean()\n",
    "femaleclean['stepsbytime'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "femaleclean['Wandering'] = (femaleclean.stepsbytime<2222) & (np.abs(femaleclean.TURNANGLE) > 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>FID</th>\n",
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       "      <th>Sex</th>\n",
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       "      <th>time_difference</th>\n",
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       "<p>5 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  FID  Id  Sex  Bear_ID  SAMPLEID   TURNANGLE  STEPLENGTH  \\\n",
       "0           1    0   0    0        7         0  122.738490  329.371060   \n",
       "1          12   11   0    0        7        11 -163.857763  596.801120   \n",
       "2          23   22   0    0        7        22 -150.260818  554.762369   \n",
       "3          34   33   0    0        7        33   96.365383  617.323955   \n",
       "4          45   44   0    0        7        44   50.005390  363.594567   \n",
       "\n",
       "      BEARING  OBSERVED  ...   NatnegLWM   NatnegMIN   NatnegMAX   NatnegBEG  \\\n",
       "0  332.177023         1  ... -256.918925 -400.249908 -108.166542 -400.249908   \n",
       "1  168.319259         1  ... -333.297213 -496.588348 -108.166542 -108.166542   \n",
       "2   18.058442         1  ... -419.938047 -550.726807 -258.069763 -416.773315   \n",
       "3  114.423824         1  ... -306.522643 -417.851654 -169.705627 -258.069763   \n",
       "4  164.429215         1  ... -151.907745 -234.307495  -60.000000 -169.705627   \n",
       "\n",
       "    NatnegEND  STRATUM     resid  time_difference  stepsbytime  Wandering  \n",
       "0 -108.166542        1  1.619778          0.00000          inf      False  \n",
       "1 -416.773315        2  1.887523          0.29166  2046.222040       True  \n",
       "2 -258.069763        3  1.712916          0.16667  3328.507642      False  \n",
       "3 -169.705627        4  1.732025          0.41597  1484.058839       True  \n",
       "4  -60.000000        5  1.726947          0.54167   671.247377       True  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "femaleclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "750"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "femaleclean.Wandering.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.max_open_warning']=40\n",
    "plt.rcParams['figure.figsize']=(10,10)\n",
    "colnames=list(femaleclean.drop(['Wandering'], axis=1).select_dtypes(exclude='O').columns.values)\n",
    "#for i in colnames[0:]:\n",
    "#    facet = sns.FacetGrid(maleclean, hue='month',aspect=2)\n",
    "#    facet.map(sns.distplot,i)\n",
    "#    facet.add_legend()\n",
    "#    facet.fig.suptitle(''.join(map(str, list([colnames.index(i)+1,\": \",i,\" Distribution By Label\"]))))\n",
    "#    plt.tight_layout()\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       40344.56042\n",
       "1       40344.85208\n",
       "2       40345.01875\n",
       "3       40345.43472\n",
       "4       40345.97639\n",
       "           ...     \n",
       "2085    41149.83403\n",
       "2086    41149.91667\n",
       "2087    41150.50000\n",
       "2088    41150.75000\n",
       "2089    41151.00000\n",
       "Name: datetime, Length: 2090, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "femaleclean.datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "femaleclean.Wandering = femaleclean.Wandering.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  FID  Id  Sex  Bear_ID  SAMPLEID     BEARING  OBSERVED  \\\n",
       "0           1    0   0    0        7         0  332.177023         1   \n",
       "1          12   11   0    0        7        11  168.319259         1   \n",
       "2          23   22   0    0        7        22   18.058442         1   \n",
       "3          34   33   0    0        7        33  114.423824         1   \n",
       "4          45   44   0    0        7        44  164.429215         1   \n",
       "\n",
       "      datetime  month  ...  naturalEND   NatnegLWM   NatnegMIN   NatnegMAX  \\\n",
       "0  40344.56042      6  ...           1 -256.918925 -400.249908 -108.166542   \n",
       "1  40344.85208      6  ...           1 -333.297213 -496.588348 -108.166542   \n",
       "2  40345.01875      6  ...           1 -419.938047 -550.726807 -258.069763   \n",
       "3  40345.43472      6  ...           1 -306.522643 -417.851654 -169.705627   \n",
       "4  40345.97639      6  ...           1 -151.907745 -234.307495  -60.000000   \n",
       "\n",
       "    NatnegBEG   NatnegEND  STRATUM     resid  time_difference  Wandering  \n",
       "0 -400.249908 -108.166542        1  1.619778          0.00000          0  \n",
       "1 -108.166542 -416.773315        2  1.887523          0.29166          1  \n",
       "2 -416.773315 -258.069763        3  1.712916          0.16667          0  \n",
       "3 -258.069763 -169.705627        4  1.732025          0.41597          1  \n",
       "4 -169.705627  -60.000000        5  1.726947          0.54167          1  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "femaleclean = femaleclean.drop(['TURNANGLE', 'STEPLENGTH', 'stepsbytime'], axis=1)\n",
    "femaleclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a holdout set\n",
    "from sklearn.model_selection import train_test_split\n",
    "df_x, unseen_x_test, df_y, unseen_y_test = train_test_split(femaleclean.loc[:, femaleclean.columns != 'Wandering'], femaleclean['Wandering'], test_size=0.30, stratify=femaleclean['Wandering'],random_state=53)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(627, 69)\n",
      "(627,)\n"
     ]
    }
   ],
   "source": [
    "print(unseen_x_test.shape)\n",
    "print(unseen_y_test.shape)\n",
    "\n",
    "df_x.reset_index(drop=True, inplace=True)\n",
    "unseen_x_test.reset_index(drop=True, inplace=True)\n",
    "df_y.reset_index(drop=True, inplace=True)\n",
    "unseen_y_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1463, 69)\n",
      "(1463,)\n"
     ]
    }
   ],
   "source": [
    "print(df_x.shape)\n",
    "print(df_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TRAIN\n",
    "#scale the test data seperately from the unseen holdout set\n",
    "from sklearn import preprocessing\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "imp = IterativeImputer(max_iter=10, random_state=0)\n",
    "\n",
    "x = df_x.loc[:, df_x.dtypes == 'float64'].values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "x = imp.fit_transform(x)\n",
    "x = pd.DataFrame(x)\n",
    "x.columns = df_x.loc[:, df_x.dtypes == 'float64'].columns\n",
    "unscaled = pd.concat([pd.DataFrame(x),df_y], axis=1)\n",
    "\n",
    "##HOLDOUT\n",
    "#scale the test data seperately from the unseen holdout set\n",
    "from sklearn import preprocessing\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "imp = IterativeImputer(max_iter=10, random_state=0)\n",
    "\n",
    "x = unseen_x_test.loc[:, unseen_x_test.dtypes == 'float64'].values #returns a numpy array\n",
    "#min_max_scaler = preprocessing.MinMaxScaler()\n",
    "#x_scaled = min_max_scaler.fit_transform(x)\n",
    "x = imp.fit_transform(x)\n",
    "x = pd.DataFrame(x)\n",
    "x.columns = unseen_x_test.loc[:, unseen_x_test.dtypes == 'float64'].columns\n",
    "x = pd.DataFrame(x)\n",
    "#one_hot = pd.get_dummies(unseen_x_test.x24)\n",
    "unseen_x = pd.concat([x], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>BEARING</th>\n",
       "      <th>datetime</th>\n",
       "      <th>disagLWM</th>\n",
       "      <th>disagMIN</th>\n",
       "      <th>disagMAX</th>\n",
       "      <th>disagBEG</th>\n",
       "      <th>disagEND</th>\n",
       "      <th>disforLWM</th>\n",
       "      <th>disforMIN</th>\n",
       "      <th>disforMAX</th>\n",
       "      <th>...</th>\n",
       "      <th>forestLWM</th>\n",
       "      <th>naturalLWM</th>\n",
       "      <th>NatnegLWM</th>\n",
       "      <th>NatnegMIN</th>\n",
       "      <th>NatnegMAX</th>\n",
       "      <th>NatnegBEG</th>\n",
       "      <th>NatnegEND</th>\n",
       "      <th>resid</th>\n",
       "      <th>time_difference</th>\n",
       "      <th>Wandering</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.114765</td>\n",
       "      <td>40444.37986</td>\n",
       "      <td>458.719445</td>\n",
       "      <td>271.66156</td>\n",
       "      <td>632.850708</td>\n",
       "      <td>271.66156</td>\n",
       "      <td>632.850708</td>\n",
       "      <td>0.651826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.978272</td>\n",
       "      <td>0.978272</td>\n",
       "      <td>-211.654907</td>\n",
       "      <td>-362.491394</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-271.66156</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.853943</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>116.650430</td>\n",
       "      <td>41148.83333</td>\n",
       "      <td>30.003699</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>67.082039</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>4.066369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.864454</td>\n",
       "      <td>0.864454</td>\n",
       "      <td>-25.937330</td>\n",
       "      <td>-67.082039</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>-60.000000</td>\n",
       "      <td>1.218977</td>\n",
       "      <td>0.16597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>271.938624</td>\n",
       "      <td>40829.00069</td>\n",
       "      <td>56.565328</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-56.565328</td>\n",
       "      <td>-60.000000</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>-30.00000</td>\n",
       "      <td>-60.000000</td>\n",
       "      <td>1.494844</td>\n",
       "      <td>0.74930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102.141768</td>\n",
       "      <td>40832.16667</td>\n",
       "      <td>17.045493</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>123.693169</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>123.693169</td>\n",
       "      <td>56.869666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239004</td>\n",
       "      <td>0.239004</td>\n",
       "      <td>47.123746</td>\n",
       "      <td>-60.000000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>-42.426407</td>\n",
       "      <td>1.417934</td>\n",
       "      <td>0.91667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>140.183810</td>\n",
       "      <td>40433.83403</td>\n",
       "      <td>37.012882</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>20.752826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415858</td>\n",
       "      <td>0.415858</td>\n",
       "      <td>-16.260056</td>\n",
       "      <td>-150.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-30.00000</td>\n",
       "      <td>-150.000000</td>\n",
       "      <td>1.615502</td>\n",
       "      <td>0.24861</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      BEARING     datetime    disagLWM   disagMIN    disagMAX   disagBEG  \\\n",
       "0   25.114765  40444.37986  458.719445  271.66156  632.850708  271.66156   \n",
       "1  116.650430  41148.83333   30.003699    0.00000   67.082039    0.00000   \n",
       "2  271.938624  40829.00069   56.565328   30.00000   60.000000   30.00000   \n",
       "3  102.141768  40832.16667   17.045493    0.00000  123.693169    0.00000   \n",
       "4  140.183810  40433.83403   37.012882    0.00000  150.000000   30.00000   \n",
       "\n",
       "     disagEND  disforLWM  disforMIN  disforMAX  ...  forestLWM  naturalLWM  \\\n",
       "0  632.850708   0.651826        0.0       30.0  ...   0.978272    0.978272   \n",
       "1   60.000000   4.066369        0.0       30.0  ...   0.864454    0.864454   \n",
       "2   60.000000   0.000000        0.0        0.0  ...   1.000000    1.000000   \n",
       "3  123.693169  56.869666        0.0      120.0  ...   0.239004    0.239004   \n",
       "4  150.000000  20.752826        0.0       60.0  ...   0.415858    0.415858   \n",
       "\n",
       "    NatnegLWM   NatnegMIN  NatnegMAX  NatnegBEG   NatnegEND     resid  \\\n",
       "0 -211.654907 -362.491394       30.0 -271.66156   30.000000  1.853943   \n",
       "1  -25.937330  -67.082039       30.0   30.00000  -60.000000  1.218977   \n",
       "2  -56.565328  -60.000000      -30.0  -30.00000  -60.000000  1.494844   \n",
       "3   47.123746  -60.000000      120.0   30.00000  -42.426407  1.417934   \n",
       "4  -16.260056 -150.000000       60.0  -30.00000 -150.000000  1.615502   \n",
       "\n",
       "   time_difference  Wandering  \n",
       "0          0.37500          0  \n",
       "1          0.16597          0  \n",
       "2          0.74930          1  \n",
       "3          0.91667          1  \n",
       "4          0.24861          1  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unscaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1463 entries, 0 to 1462\n",
      "Data columns (total 52 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   BEARING          1463 non-null   float64\n",
      " 1   datetime         1463 non-null   float64\n",
      " 2   disagLWM         1463 non-null   float64\n",
      " 3   disagMIN         1463 non-null   float64\n",
      " 4   disagMAX         1463 non-null   float64\n",
      " 5   disagBEG         1463 non-null   float64\n",
      " 6   disagEND         1463 non-null   float64\n",
      " 7   disforLWM        1463 non-null   float64\n",
      " 8   disforMIN        1463 non-null   float64\n",
      " 9   disforMAX        1463 non-null   float64\n",
      " 10  disforBEG        1463 non-null   float64\n",
      " 11  disforEND        1463 non-null   float64\n",
      " 12  disnatLWM        1463 non-null   float64\n",
      " 13  disnatMIN        1463 non-null   float64\n",
      " 14  disnatMAX        1463 non-null   float64\n",
      " 15  disnatBEG        1463 non-null   float64\n",
      " 16  disnatEND        1463 non-null   float64\n",
      " 17  distrdsLWM       1463 non-null   float64\n",
      " 18  distrdsMIN       1463 non-null   float64\n",
      " 19  distrdsMAX       1463 non-null   float64\n",
      " 20  distrdsBEG       1463 non-null   float64\n",
      " 21  distrdsEND       1463 non-null   float64\n",
      " 22  distwatLWM       1463 non-null   float64\n",
      " 23  distwatMIN       1463 non-null   float64\n",
      " 24  distwatMAX       1463 non-null   float64\n",
      " 25  distwatBEG       1463 non-null   float64\n",
      " 26  distwatEND       1463 non-null   float64\n",
      " 27  natden5LWM       1463 non-null   float64\n",
      " 28  natden5MIN       1463 non-null   float64\n",
      " 29  natden5MAX       1463 non-null   float64\n",
      " 30  natden5BEG       1463 non-null   float64\n",
      " 31  natden5END       1463 non-null   float64\n",
      " 32  fordenLWM        1463 non-null   float64\n",
      " 33  fordenMIN        1463 non-null   float64\n",
      " 34  fordenMAX        1463 non-null   float64\n",
      " 35  fordenBEG        1463 non-null   float64\n",
      " 36  fordenEND        1463 non-null   float64\n",
      " 37  rddenLWM         1463 non-null   float64\n",
      " 38  rddenMIN         1463 non-null   float64\n",
      " 39  rddenMAX         1463 non-null   float64\n",
      " 40  rddenBEG         1463 non-null   float64\n",
      " 41  rddenEND         1463 non-null   float64\n",
      " 42  forestLWM        1463 non-null   float64\n",
      " 43  naturalLWM       1463 non-null   float64\n",
      " 44  NatnegLWM        1463 non-null   float64\n",
      " 45  NatnegMIN        1463 non-null   float64\n",
      " 46  NatnegMAX        1463 non-null   float64\n",
      " 47  NatnegBEG        1463 non-null   float64\n",
      " 48  NatnegEND        1463 non-null   float64\n",
      " 49  resid            1463 non-null   float64\n",
      " 50  time_difference  1463 non-null   float64\n",
      " 51  Wandering        1463 non-null   int64  \n",
      "dtypes: float64(51), int64(1)\n",
      "memory usage: 594.5 KB\n"
     ]
    }
   ],
   "source": [
    "unscaled.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install pycaret\n",
    "from lightgbm import LGBMClassifier as lgbmc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_be67907c_552b_11eb_9dc1_acde48001122row29_col1,#T_be67907c_552b_11eb_9dc1_acde48001122row42_col1,#T_be67907c_552b_11eb_9dc1_acde48001122row51_col1,#T_be67907c_552b_11eb_9dc1_acde48001122row53_col1{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_be67907c_552b_11eb_9dc1_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row0_col1\" class=\"data row0 col1\" >6054</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row1_col1\" class=\"data row1 col1\" >Wandering</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row3_col1\" class=\"data row3 col1\" >0: 0, 1: 1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row4_col1\" class=\"data row4 col1\" >(1463, 52)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row6_col1\" class=\"data row6 col1\" >51</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row7_col1\" class=\"data row7 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row9_col1\" class=\"data row9 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row10_col1\" class=\"data row10 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row11_col1\" class=\"data row11 col1\" >(1024, 40)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row12_col1\" class=\"data row12 col1\" >(439, 40)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row16_col1\" class=\"data row16 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row17_col1\" class=\"data row17 col1\" >42</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row20_col1\" class=\"data row20 col1\" >clf-default-name</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row21_col1\" class=\"data row21 col1\" >ee74</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row22_col1\" class=\"data row22 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row24_col1\" class=\"data row24 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row26_col1\" class=\"data row26 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row29_col1\" class=\"data row29 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row30_col1\" class=\"data row30 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row33_col1\" class=\"data row33 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row34_col1\" class=\"data row34 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row42_col1\" class=\"data row42 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row43_col1\" class=\"data row43 col1\" >0.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row44_col0\" class=\"data row44 col0\" >Clustering</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row44_col1\" class=\"data row44 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row45_col0\" class=\"data row45 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row45_col1\" class=\"data row45 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row46_col0\" class=\"data row46 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row47_col0\" class=\"data row47 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row48_col0\" class=\"data row48 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row48_col1\" class=\"data row48 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row49_col0\" class=\"data row49 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row49_col1\" class=\"data row49 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row50_col0\" class=\"data row50 col0\" >Group Features</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row50_col1\" class=\"data row50 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row51_col0\" class=\"data row51 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row51_col1\" class=\"data row51 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row52_col0\" class=\"data row52 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row52_col1\" class=\"data row52 col1\" >0.800000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row53_col0\" class=\"data row53 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row53_col1\" class=\"data row53 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row54_col0\" class=\"data row54 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row54_col1\" class=\"data row54 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row55_col0\" class=\"data row55 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row55_col1\" class=\"data row55 col1\" >0.010000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row56_col0\" class=\"data row56 col0\" >Fix Imbalance</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_be67907c_552b_11eb_9dc1_acde48001122level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row57_col0\" class=\"data row57 col0\" >Fix Imbalance Method</td>\n",
       "                        <td id=\"T_be67907c_552b_11eb_9dc1_acde48001122row57_col1\" class=\"data row57 col1\" >SMOTE</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x132c1c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.classification import *\n",
    "#intialize the setup\n",
    "\n",
    "exp_clf = setup(unscaled\n",
    "                ,target = 'Wandering'\n",
    "                ,train_size = 0.7\n",
    "                ,profile = False\n",
    "                ,remove_multicollinearity = True\n",
    "                ,polynomial_features = False\n",
    "                ,feature_interaction = True\n",
    "                ,feature_ratio = False\n",
    "                ,feature_selection = True\n",
    "                ,fix_imbalance = False\n",
    "                ,silent=True\n",
    "                #,cluster = True\n",
    "                ,pca = False\n",
    "                ,numeric_imputation = 'mean'\n",
    "                ,normalize=True\n",
    "                ,n_jobs=42\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            text-align:  left;\n",
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       "        }</style><table id=\"T_2a964266_552c_11eb_9dc1_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col0\" class=\"data row0 col0\" >CatBoost Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col1\" class=\"data row0 col1\" >0.7647</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col2\" class=\"data row0 col2\" >0.8465</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col3\" class=\"data row0 col3\" >0.7359</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col4\" class=\"data row0 col4\" >0.7622</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col5\" class=\"data row0 col5\" >0.7625</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col6\" class=\"data row0 col6\" >0.4787</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col7\" class=\"data row0 col7\" >0.4801</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row0_col8\" class=\"data row0 col8\" >5.7930</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row1\" class=\"row_heading level0 row1\" >gbc</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col0\" class=\"data row1 col0\" >Gradient Boosting Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col1\" class=\"data row1 col1\" >0.7618</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col2\" class=\"data row1 col2\" >0.8351</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col3\" class=\"data row1 col3\" >0.7283</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col4\" class=\"data row1 col4\" >0.7580</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col5\" class=\"data row1 col5\" >0.7581</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col6\" class=\"data row1 col6\" >0.4673</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col7\" class=\"data row1 col7\" >0.4698</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row1_col8\" class=\"data row1 col8\" >0.4220</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col1\" class=\"data row2 col1\" >0.7588</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col2\" class=\"data row2 col2\" >0.8371</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col3\" class=\"data row2 col3\" >0.7259</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col4\" class=\"data row2 col4\" >0.7558</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col5\" class=\"data row2 col5\" >0.7551</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col6\" class=\"data row2 col6\" >0.4614</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col7\" class=\"data row2 col7\" >0.4644</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row2_col8\" class=\"data row2 col8\" >0.2250</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row3\" class=\"row_heading level0 row3\" >et</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col0\" class=\"data row3 col0\" >Extra Trees Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col1\" class=\"data row3 col1\" >0.7511</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col2\" class=\"data row3 col2\" >0.8221</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col3\" class=\"data row3 col3\" >0.7045</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col4\" class=\"data row3 col4\" >0.7445</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col5\" class=\"data row3 col5\" >0.7425</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col6\" class=\"data row3 col6\" >0.4287</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col7\" class=\"data row3 col7\" >0.4357</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row3_col8\" class=\"data row3 col8\" >0.6760</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col1\" class=\"data row4 col1\" >0.7481</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col2\" class=\"data row4 col2\" >0.8209</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col3\" class=\"data row4 col3\" >0.7144</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col4\" class=\"data row4 col4\" >0.7439</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col5\" class=\"data row4 col5\" >0.7448</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col6\" class=\"data row4 col6\" >0.4384</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col7\" class=\"data row4 col7\" >0.4400</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row4_col8\" class=\"data row4 col8\" >0.7140</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row5\" class=\"row_heading level0 row5\" >ada</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col0\" class=\"data row5 col0\" >Ada Boost Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col1\" class=\"data row5 col1\" >0.7471</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col2\" class=\"data row5 col2\" >0.8131</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col3\" class=\"data row5 col3\" >0.7100</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col4\" class=\"data row5 col4\" >0.7424</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col5\" class=\"data row5 col5\" >0.7426</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col6\" class=\"data row5 col6\" >0.4325</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col7\" class=\"data row5 col7\" >0.4355</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row5_col8\" class=\"data row5 col8\" >0.3080</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row6\" class=\"row_heading level0 row6\" >lightgbm</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col0\" class=\"data row6 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col1\" class=\"data row6 col1\" >0.7384</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col2\" class=\"data row6 col2\" >0.8254</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col3\" class=\"data row6 col3\" >0.7040</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col4\" class=\"data row6 col4\" >0.7344</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col5\" class=\"data row6 col5\" >0.7347</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col6\" class=\"data row6 col6\" >0.4167</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col7\" class=\"data row6 col7\" >0.4188</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row6_col8\" class=\"data row6 col8\" >1.8220</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row7\" class=\"row_heading level0 row7\" >qda</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col0\" class=\"data row7 col0\" >Quadratic Discriminant Analysis</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col1\" class=\"data row7 col1\" >0.7129</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col2\" class=\"data row7 col2\" >0.7930</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col3\" class=\"data row7 col3\" >0.6535</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col4\" class=\"data row7 col4\" >0.7201</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col5\" class=\"data row7 col5\" >0.6897</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col6\" class=\"data row7 col6\" >0.3235</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col7\" class=\"data row7 col7\" >0.3533</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row7_col8\" class=\"data row7 col8\" >0.3200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row8\" class=\"row_heading level0 row8\" >dt</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col0\" class=\"data row8 col0\" >Decision Tree Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col1\" class=\"data row8 col1\" >0.7070</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col2\" class=\"data row8 col2\" >0.6801</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col3\" class=\"data row8 col3\" >0.6801</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col4\" class=\"data row8 col4\" >0.7087</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col5\" class=\"data row8 col5\" >0.7065</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col6\" class=\"data row8 col6\" >0.3616</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col7\" class=\"data row8 col7\" >0.3632</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row8_col8\" class=\"data row8 col8\" >0.3530</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row9\" class=\"row_heading level0 row9\" >lda</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col0\" class=\"data row9 col0\" >Linear Discriminant Analysis</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col1\" class=\"data row9 col1\" >0.7041</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col2\" class=\"data row9 col2\" >0.7435</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col3\" class=\"data row9 col3\" >0.6454</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col4\" class=\"data row9 col4\" >0.6944</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col5\" class=\"data row9 col5\" >0.6896</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col6\" class=\"data row9 col6\" >0.3108</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col7\" class=\"data row9 col7\" >0.3220</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row9_col8\" class=\"data row9 col8\" >0.1150</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col0\" class=\"data row10 col0\" >Ridge Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col1\" class=\"data row10 col1\" >0.6924</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col2\" class=\"data row10 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col3\" class=\"data row10 col3\" >0.6236</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col4\" class=\"data row10 col4\" >0.6801</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col5\" class=\"data row10 col5\" >0.6714</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col6\" class=\"data row10 col6\" >0.2695</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col7\" class=\"data row10 col7\" >0.2855</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row10_col8\" class=\"data row10 col8\" >0.2990</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row11\" class=\"row_heading level0 row11\" >lr</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col0\" class=\"data row11 col0\" >Logistic Regression</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col1\" class=\"data row11 col1\" >0.6876</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col2\" class=\"data row11 col2\" >0.7273</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col3\" class=\"data row11 col3\" >0.6278</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col4\" class=\"data row11 col4\" >0.6762</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col5\" class=\"data row11 col5\" >0.6730</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col6\" class=\"data row11 col6\" >0.2737</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col7\" class=\"data row11 col7\" >0.2837</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row11_col8\" class=\"data row11 col8\" >0.6410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col1\" class=\"data row12 col1\" >0.6543</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col2\" class=\"data row12 col2\" >0.6602</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col3\" class=\"data row12 col3\" >0.6170</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col4\" class=\"data row12 col4\" >0.6529</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col5\" class=\"data row12 col5\" >0.6506</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col6\" class=\"data row12 col6\" >0.2385</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col7\" class=\"data row12 col7\" >0.2408</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row12_col8\" class=\"data row12 col8\" >0.7430</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row13\" class=\"row_heading level0 row13\" >nb</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col0\" class=\"data row13 col0\" >Naive Bayes</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col1\" class=\"data row13 col1\" >0.6484</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col2\" class=\"data row13 col2\" >0.6743</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col3\" class=\"data row13 col3\" >0.5154</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col4\" class=\"data row13 col4\" >0.6612</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col5\" class=\"data row13 col5\" >0.5369</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col6\" class=\"data row13 col6\" >0.0387</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col7\" class=\"data row13 col7\" >0.0982</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row13_col8\" class=\"data row13 col8\" >0.3490</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2a964266_552c_11eb_9dc1_acde48001122level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col1\" class=\"data row14 col1\" >0.6328</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col3\" class=\"data row14 col3\" >0.5997</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col4\" class=\"data row14 col4\" >0.6405</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col5\" class=\"data row14 col5\" >0.6246</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col6\" class=\"data row14 col6\" >0.1981</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col7\" class=\"data row14 col7\" >0.2065</td>\n",
       "                        <td id=\"T_2a964266_552c_11eb_9dc1_acde48001122row14_col8\" class=\"data row14 col8\" >0.2900</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x132c14c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare all baseline models and select top 5\n",
    "top5 = compare_models(n_select = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col0,#T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col1,#T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col2,#T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col3,#T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col4,#T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col5,#T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col6{\n",
       "            background:  yellow;\n",
       "        }</style><table id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row0_col0\" class=\"data row0 col0\" >0.7379</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row0_col1\" class=\"data row0 col1\" >0.8350</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row0_col2\" class=\"data row0 col2\" >0.7064</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row0_col3\" class=\"data row0 col3\" >0.7339</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row0_col4\" class=\"data row0 col4\" >0.7352</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row0_col5\" class=\"data row0 col5\" >0.4203</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row0_col6\" class=\"data row0 col6\" >0.4211</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row1_col0\" class=\"data row1 col0\" >0.7961</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row1_col1\" class=\"data row1 col1\" >0.8591</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row1_col2\" class=\"data row1 col2\" >0.7697</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row1_col3\" class=\"data row1 col3\" >0.7935</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row1_col4\" class=\"data row1 col4\" >0.7941</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row1_col5\" class=\"data row1 col5\" >0.5491</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row1_col6\" class=\"data row1 col6\" >0.5502</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row2_col0\" class=\"data row2 col0\" >0.7767</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row2_col1\" class=\"data row2 col1\" >0.8636</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row2_col2\" class=\"data row2 col2\" >0.7426</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row2_col3\" class=\"data row2 col3\" >0.7726</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row2_col4\" class=\"data row2 col4\" >0.7727</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row2_col5\" class=\"data row2 col5\" >0.5001</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row2_col6\" class=\"data row2 col6\" >0.5031</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row3_col0\" class=\"data row3 col0\" >0.7087</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row3_col1\" class=\"data row3 col1\" >0.7887</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row3_col2\" class=\"data row3 col2\" >0.6777</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row3_col3\" class=\"data row3 col3\" >0.7055</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row3_col4\" class=\"data row3 col4\" >0.7069</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row3_col5\" class=\"data row3 col5\" >0.3597</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row3_col6\" class=\"data row3 col6\" >0.3600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row4_col0\" class=\"data row4 col0\" >0.7549</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row4_col1\" class=\"data row4 col1\" >0.8565</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row4_col2\" class=\"data row4 col2\" >0.7096</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row4_col3\" class=\"data row4 col3\" >0.7486</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row4_col4\" class=\"data row4 col4\" >0.7479</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row4_col5\" class=\"data row4 col5\" >0.4386</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row4_col6\" class=\"data row4 col6\" >0.4441</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row5_col0\" class=\"data row5 col0\" >0.7549</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row5_col1\" class=\"data row5 col1\" >0.8316</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row5_col2\" class=\"data row5 col2\" >0.7285</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row5_col3\" class=\"data row5 col3\" >0.7534</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row5_col4\" class=\"data row5 col4\" >0.7541</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row5_col5\" class=\"data row5 col5\" >0.4600</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row5_col6\" class=\"data row5 col6\" >0.4601</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row6_col0\" class=\"data row6 col0\" >0.7647</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row6_col1\" class=\"data row6 col1\" >0.8359</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row6_col2\" class=\"data row6 col2\" >0.7424</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row6_col3\" class=\"data row6 col3\" >0.7647</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row6_col4\" class=\"data row6 col4\" >0.7647</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row6_col5\" class=\"data row6 col5\" >0.4848</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row6_col6\" class=\"data row6 col6\" >0.4848</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row7_col0\" class=\"data row7 col0\" >0.7549</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row7_col1\" class=\"data row7 col1\" >0.8556</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row7_col2\" class=\"data row7 col2\" >0.7159</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row7_col3\" class=\"data row7 col3\" >0.7494</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row7_col4\" class=\"data row7 col4\" >0.7502</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row7_col5\" class=\"data row7 col5\" >0.4459</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row7_col6\" class=\"data row7 col6\" >0.4487</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row8_col0\" class=\"data row8 col0\" >0.7843</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row8_col1\" class=\"data row8 col1\" >0.8380</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row8_col2\" class=\"data row8 col2\" >0.7702</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row8_col3\" class=\"data row8 col3\" >0.7874</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row8_col4\" class=\"data row8 col4\" >0.7856</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row8_col5\" class=\"data row8 col5\" >0.5337</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row8_col6\" class=\"data row8 col6\" >0.5341</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row9_col0\" class=\"data row9 col0\" >0.8137</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row9_col1\" class=\"data row9 col1\" >0.9015</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row9_col2\" class=\"data row9 col2\" >0.7956</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row9_col3\" class=\"data row9 col3\" >0.8128</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row9_col4\" class=\"data row9 col4\" >0.8132</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row9_col5\" class=\"data row9 col5\" >0.5947</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row9_col6\" class=\"data row9 col6\" >0.5949</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col0\" class=\"data row10 col0\" >0.7647</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col1\" class=\"data row10 col1\" >0.8465</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col2\" class=\"data row10 col2\" >0.7359</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col3\" class=\"data row10 col3\" >0.7622</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col4\" class=\"data row10 col4\" >0.7625</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col5\" class=\"data row10 col5\" >0.4787</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row10_col6\" class=\"data row10 col6\" >0.4801</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row11_col0\" class=\"data row11 col0\" >0.0284</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row11_col1\" class=\"data row11 col1\" >0.0275</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row11_col2\" class=\"data row11 col2\" >0.0337</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row11_col3\" class=\"data row11 col3\" >0.0295</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row11_col4\" class=\"data row11 col4\" >0.0291</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row11_col5\" class=\"data row11 col5\" >0.0652</td>\n",
       "                        <td id=\"T_54cbf45e_552c_11eb_9dc1_acde48001122row11_col6\" class=\"data row11 col6\" >0.0649</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x132dd7350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catboost = create_model(estimator = 'catboost')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select best model \n",
    "best = automl(optimize = 'MCC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best, plot='pr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best, plot='confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best, plot='class_report')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best, plot='boundary')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nan_mode</th>\n",
       "      <td>Min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eval_metric</th>\n",
       "      <td>Logloss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iterations</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sampling_frequency</th>\n",
       "      <td>PerTree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leaf_estimation_method</th>\n",
       "      <td>Newton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grow_policy</th>\n",
       "      <td>SymmetricTree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>penalties_coefficient</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boosting_type</th>\n",
       "      <td>Plain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_shrink_mode</th>\n",
       "      <td>Constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_border_type</th>\n",
       "      <td>GreedyLogSum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bayesian_matrix_reg</th>\n",
       "      <td>0.10000000149011612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l2_leaf_reg</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_strength</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsm</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boost_from_average</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_size_reg</th>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsample</th>\n",
       "      <td>0.800000011920929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>use_best_model</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_names</th>\n",
       "      <td>[0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_seed</th>\n",
       "      <td>6054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>posterior_sampling</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>border_count</th>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classes_count</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auto_class_weights</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sparse_features_conflict_fraction</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leaf_estimation_backtracking</th>\n",
       "      <td>AnyImprovement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best_model_min_trees</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_shrink_rate</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_data_in_leaf</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss_function</th>\n",
       "      <td>Logloss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning_rate</th>\n",
       "      <td>0.010406999848783016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score_function</th>\n",
       "      <td>Cosine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>task_type</th>\n",
       "      <td>CPU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leaf_estimation_iterations</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bootstrap_type</th>\n",
       "      <td>MVS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_leaves</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Parameters\n",
       "nan_mode                                            Min\n",
       "eval_metric                                     Logloss\n",
       "iterations                                         1000\n",
       "sampling_frequency                              PerTree\n",
       "leaf_estimation_method                           Newton\n",
       "grow_policy                               SymmetricTree\n",
       "penalties_coefficient                                 1\n",
       "boosting_type                                     Plain\n",
       "model_shrink_mode                              Constant\n",
       "feature_border_type                        GreedyLogSum\n",
       "bayesian_matrix_reg                 0.10000000149011612\n",
       "l2_leaf_reg                                           3\n",
       "random_strength                                       1\n",
       "rsm                                                   1\n",
       "boost_from_average                                False\n",
       "model_size_reg                                      0.5\n",
       "subsample                             0.800000011920929\n",
       "use_best_model                                    False\n",
       "class_names                                      [0, 1]\n",
       "random_seed                                        6054\n",
       "depth                                                 6\n",
       "posterior_sampling                                False\n",
       "border_count                                        254\n",
       "classes_count                                         0\n",
       "auto_class_weights                                 None\n",
       "sparse_features_conflict_fraction                     0\n",
       "leaf_estimation_backtracking             AnyImprovement\n",
       "best_model_min_trees                                  1\n",
       "model_shrink_rate                                     0\n",
       "min_data_in_leaf                                      1\n",
       "loss_function                                   Logloss\n",
       "learning_rate                      0.010406999848783016\n",
       "score_function                                   Cosine\n",
       "task_type                                           CPU\n",
       "leaf_estimation_iterations                           10\n",
       "bootstrap_type                                      MVS\n",
       "max_leaves                                           64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best, plot='parameter')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(best, plot='feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}