7_pycaret.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41185 entries, 0 to 41184\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   age               41185 non-null  int64  \n",
      " 1   job               41185 non-null  object \n",
      " 2   marital           41185 non-null  object \n",
      " 3   education         41185 non-null  object \n",
      " 4   default           41185 non-null  object \n",
      " 5   housing           41185 non-null  object \n",
      " 6   loan              41185 non-null  object \n",
      " 7   contact           41185 non-null  object \n",
      " 8   month             41185 non-null  object \n",
      " 9   day_of_week       41185 non-null  object \n",
      " 10  campaign          41185 non-null  int64  \n",
      " 11  contacted_before  41185 non-null  int64  \n",
      " 12  previous          41185 non-null  int64  \n",
      " 13  poutcome          41185 non-null  object \n",
      " 14  emp.var.rate      41185 non-null  float64\n",
      " 15  cons.price.idx    41185 non-null  int64  \n",
      " 16  cons.conf.idx     41185 non-null  int64  \n",
      " 17  euribor3m         41185 non-null  int64  \n",
      " 18  nr.employed       41185 non-null  int64  \n",
      " 19  subscribed        41185 non-null  int64  \n",
      "dtypes: float64(1), int64(9), object(10)\n",
      "memory usage: 6.3+ MB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>campaign</th>\n",
       "      <th>contacted_before</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  campaign  contacted_before  previous     poutcome  \\\n",
       "0   may         mon         1                 0         0  nonexistent   \n",
       "1   may         mon         1                 0         0  nonexistent   \n",
       "2   may         mon         1                 0         0  nonexistent   \n",
       "3   may         mon         1                 0         0  nonexistent   \n",
       "4   may         mon         1                 0         0  nonexistent   \n",
       "\n",
       "   emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \\\n",
       "0           3.0               3              2          3            2   \n",
       "1           3.0               3              2          3            2   \n",
       "2           3.0               3              2          3            2   \n",
       "3           3.0               3              2          3            2   \n",
       "4           3.0               3              2          3            2   \n",
       "\n",
       "   subscribed  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "data = pd.read_csv('data/model_data.csv')\n",
    "\n",
    "data.info()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Balance data using smotenc before setup and encoding\n",
    "from imblearn.over_sampling import SMOTE,SMOTENC\n",
    "from sklearn.model_selection import train_test_split\n",
    "smotenc = SMOTENC(categorical_features=[1,2,3,4,5,6,7,8,9,11,12,13],)\n",
    "\n",
    "X=data.drop(labels=['subscribed'], axis=1)\n",
    "y=data['subscribed']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)\n",
    "\n",
    "X_train, y_train = smotenc.fit_resample(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51190\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>campaign</th>\n",
       "      <th>contacted_before</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cellular</th>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
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       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>telephone</th>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
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       "      <td>12596</td>\n",
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       "      <td>12596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             age    job  marital  education  default  housing   loan  month  \\\n",
       "contact                                                                       \n",
       "cellular   38594  38594    38594      38594    38594    38594  38594  38594   \n",
       "telephone  12596  12596    12596      12596    12596    12596  12596  12596   \n",
       "\n",
       "           day_of_week  campaign  contacted_before  previous  poutcome  \\\n",
       "contact                                                                  \n",
       "cellular         38594     38594             38594     38594     38594   \n",
       "telephone        12596     12596             12596     12596     12596   \n",
       "\n",
       "           emp.var.rate  cons.price.idx  cons.conf.idx  nr.employed  \n",
       "contact                                                              \n",
       "cellular          38594           38594          38594        38594  \n",
       "telephone         12596           12596          12596        12596  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this step is necessar because we are using the already splot data inside the pycaret setup, and indexes must not overlap on the train and test data sets\n",
    "\n",
    "#combine balanced train data back into a single df\n",
    "X_train['subscribed'] = y_train\n",
    "train_data = X_train\n",
    "\n",
    "print(len(X_train))\n",
    "#combine test data back into a single df\n",
    "X_test['subscribed'] = y_test\n",
    "test_data = X_test.set_index( np.arange(len(X_train), len(X_train)+len(X_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table id=\"T_dab1f_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_dab1f_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_dab1f_row0_col1\" class=\"data row0 col1\" >420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_dab1f_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_dab1f_row1_col1\" class=\"data row1 col1\" >subscribed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_dab1f_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_dab1f_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_dab1f_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_dab1f_row3_col1\" class=\"data row3 col1\" >(63546, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_dab1f_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_dab1f_row4_col1\" class=\"data row4 col1\" >(63546, 54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_dab1f_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_dab1f_row5_col1\" class=\"data row5 col1\" >(51190, 54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_dab1f_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_dab1f_row6_col1\" class=\"data row6 col1\" >(12356, 54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_dab1f_row7_col0\" class=\"data row7 col0\" >Ignore features</td>\n",
       "      <td id=\"T_dab1f_row7_col1\" class=\"data row7 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_dab1f_row8_col0\" class=\"data row8 col0\" >Ordinal features</td>\n",
       "      <td id=\"T_dab1f_row8_col1\" class=\"data row8 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_dab1f_row9_col0\" class=\"data row9 col0\" >Numeric features</td>\n",
       "      <td id=\"T_dab1f_row9_col1\" class=\"data row9 col1\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_dab1f_row10_col0\" class=\"data row10 col0\" >Categorical features</td>\n",
       "      <td id=\"T_dab1f_row10_col1\" class=\"data row10 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_dab1f_row11_col0\" class=\"data row11 col0\" >Preprocess</td>\n",
       "      <td id=\"T_dab1f_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_dab1f_row12_col0\" class=\"data row12 col0\" >Imputation type</td>\n",
       "      <td id=\"T_dab1f_row12_col1\" class=\"data row12 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_dab1f_row13_col0\" class=\"data row13 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_dab1f_row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_dab1f_row14_col0\" class=\"data row14 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_dab1f_row14_col1\" class=\"data row14 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_dab1f_row15_col0\" class=\"data row15 col0\" >Maximum one-hot encoding</td>\n",
       "      <td id=\"T_dab1f_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_dab1f_row16_col0\" class=\"data row16 col0\" >Encoding method</td>\n",
       "      <td id=\"T_dab1f_row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_dab1f_row17_col0\" class=\"data row17 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_dab1f_row17_col1\" class=\"data row17 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_dab1f_row18_col0\" class=\"data row18 col0\" >Fold Number</td>\n",
       "      <td id=\"T_dab1f_row18_col1\" class=\"data row18 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_dab1f_row19_col0\" class=\"data row19 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_dab1f_row19_col1\" class=\"data row19 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_dab1f_row20_col0\" class=\"data row20 col0\" >Use GPU</td>\n",
       "      <td id=\"T_dab1f_row20_col1\" class=\"data row20 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_dab1f_row21_col0\" class=\"data row21 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_dab1f_row21_col1\" class=\"data row21 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_dab1f_row22_col0\" class=\"data row22 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_dab1f_row22_col1\" class=\"data row22 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_dab1f_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_dab1f_row23_col0\" class=\"data row23 col0\" >USI</td>\n",
       "      <td id=\"T_dab1f_row23_col1\" class=\"data row23 col1\" >8218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2310af28370>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.classification import *\n",
    "s = (setup( data=train_data,\n",
    "            target = 'subscribed',\n",
    "            categorical_features=['job','marital','education','default','housing','loan','contact','poutcome','month','day_of_week'],\n",
    "            ignore_features=['euribor3m'],\n",
    "            max_encoding_ohe=-1,\n",
    "            test_data=test_data,\n",
    "            # feature_selection=True,\n",
    "            # feature_selection_method='univariate',\n",
    "            # n_features_to_select=15,\n",
    "            fold=10,\n",
    "            session_id = 420,\n",
    "            ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>age</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_admin.</th>\n",
       "      <th>job_management</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_housemaid</th>\n",
       "      <th>job_unemployed</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>contacted_before</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>...</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "      <td>38594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>...</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "      <td>12596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  job_blue-collar  job_services  job_entrepreneur  job_admin.  \\\n",
       "contact                                                                       \n",
       "0.0      38594            38594         38594             38594       38594   \n",
       "1.0      12596            12596         12596             12596       12596   \n",
       "\n",
       "         job_management  job_technician  job_housemaid  job_unemployed  \\\n",
       "contact                                                                  \n",
       "0.0               38594           38594          38594           38594   \n",
       "1.0               12596           12596          12596           12596   \n",
       "\n",
       "         job_retired  ...  campaign  contacted_before  previous  \\\n",
       "contact               ...                                         \n",
       "0.0            38594  ...     38594             38594     38594   \n",
       "1.0            12596  ...     12596             12596     12596   \n",
       "\n",
       "         poutcome_nonexistent  poutcome_failure  poutcome_success  \\\n",
       "contact                                                             \n",
       "0.0                     38594             38594             38594   \n",
       "1.0                     12596             12596             12596   \n",
       "\n",
       "         emp.var.rate  cons.price.idx  cons.conf.idx  nr.employed  \n",
       "contact                                                            \n",
       "0.0             38594           38594          38594        38594  \n",
       "1.0             12596           12596          12596        12596  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.X_train_transformed.groupby('contact').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#s.dataset_transformed.info()\n",
    "s.X_train_transformed.to_csv('./data/X_train_transformed.csv', index=False)\n",
    "s.X_test_transformed.to_csv('./data/X_test_transformed.csv', index=False)\n",
    "\n",
    "s.y_train_transformed.to_csv('./data/y_train_transformed.csv', index=False)\n",
    "s.y_test_transformed.to_csv('./data/y_test_transformed.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.X_train_transformed.groupby('job_student').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f0a3d_row10_col0, #T_f0a3d_row10_col1, #T_f0a3d_row10_col2, #T_f0a3d_row10_col3, #T_f0a3d_row10_col4, #T_f0a3d_row10_col5, #T_f0a3d_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f0a3d_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f0a3d_row0_col0\" class=\"data row0 col0\" >0.6906</td>\n",
       "      <td id=\"T_f0a3d_row0_col1\" class=\"data row0 col1\" >0.7352</td>\n",
       "      <td id=\"T_f0a3d_row0_col2\" class=\"data row0 col2\" >0.5952</td>\n",
       "      <td id=\"T_f0a3d_row0_col3\" class=\"data row0 col3\" >0.7354</td>\n",
       "      <td id=\"T_f0a3d_row0_col4\" class=\"data row0 col4\" >0.6579</td>\n",
       "      <td id=\"T_f0a3d_row0_col5\" class=\"data row0 col5\" >0.3811</td>\n",
       "      <td id=\"T_f0a3d_row0_col6\" class=\"data row0 col6\" >0.3882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f0a3d_row1_col0\" class=\"data row1 col0\" >0.7416</td>\n",
       "      <td id=\"T_f0a3d_row1_col1\" class=\"data row1 col1\" >0.8212</td>\n",
       "      <td id=\"T_f0a3d_row1_col2\" class=\"data row1 col2\" >0.6932</td>\n",
       "      <td id=\"T_f0a3d_row1_col3\" class=\"data row1 col3\" >0.7673</td>\n",
       "      <td id=\"T_f0a3d_row1_col4\" class=\"data row1 col4\" >0.7284</td>\n",
       "      <td id=\"T_f0a3d_row1_col5\" class=\"data row1 col5\" >0.4831</td>\n",
       "      <td id=\"T_f0a3d_row1_col6\" class=\"data row1 col6\" >0.4854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f0a3d_row2_col0\" class=\"data row2 col0\" >0.7662</td>\n",
       "      <td id=\"T_f0a3d_row2_col1\" class=\"data row2 col1\" >0.8518</td>\n",
       "      <td id=\"T_f0a3d_row2_col2\" class=\"data row2 col2\" >0.7253</td>\n",
       "      <td id=\"T_f0a3d_row2_col3\" class=\"data row2 col3\" >0.7898</td>\n",
       "      <td id=\"T_f0a3d_row2_col4\" class=\"data row2 col4\" >0.7562</td>\n",
       "      <td id=\"T_f0a3d_row2_col5\" class=\"data row2 col5\" >0.5323</td>\n",
       "      <td id=\"T_f0a3d_row2_col6\" class=\"data row2 col6\" >0.5341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f0a3d_row3_col0\" class=\"data row3 col0\" >0.7671</td>\n",
       "      <td id=\"T_f0a3d_row3_col1\" class=\"data row3 col1\" >0.8536</td>\n",
       "      <td id=\"T_f0a3d_row3_col2\" class=\"data row3 col2\" >0.7194</td>\n",
       "      <td id=\"T_f0a3d_row3_col3\" class=\"data row3 col3\" >0.7952</td>\n",
       "      <td id=\"T_f0a3d_row3_col4\" class=\"data row3 col4\" >0.7554</td>\n",
       "      <td id=\"T_f0a3d_row3_col5\" class=\"data row3 col5\" >0.5343</td>\n",
       "      <td id=\"T_f0a3d_row3_col6\" class=\"data row3 col6\" >0.5367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f0a3d_row4_col0\" class=\"data row4 col0\" >0.7603</td>\n",
       "      <td id=\"T_f0a3d_row4_col1\" class=\"data row4 col1\" >0.8451</td>\n",
       "      <td id=\"T_f0a3d_row4_col2\" class=\"data row4 col2\" >0.7308</td>\n",
       "      <td id=\"T_f0a3d_row4_col3\" class=\"data row4 col3\" >0.7766</td>\n",
       "      <td id=\"T_f0a3d_row4_col4\" class=\"data row4 col4\" >0.7530</td>\n",
       "      <td id=\"T_f0a3d_row4_col5\" class=\"data row4 col5\" >0.5206</td>\n",
       "      <td id=\"T_f0a3d_row4_col6\" class=\"data row4 col6\" >0.5215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_f0a3d_row5_col0\" class=\"data row5 col0\" >0.7666</td>\n",
       "      <td id=\"T_f0a3d_row5_col1\" class=\"data row5 col1\" >0.8487</td>\n",
       "      <td id=\"T_f0a3d_row5_col2\" class=\"data row5 col2\" >0.7168</td>\n",
       "      <td id=\"T_f0a3d_row5_col3\" class=\"data row5 col3\" >0.7961</td>\n",
       "      <td id=\"T_f0a3d_row5_col4\" class=\"data row5 col4\" >0.7544</td>\n",
       "      <td id=\"T_f0a3d_row5_col5\" class=\"data row5 col5\" >0.5331</td>\n",
       "      <td id=\"T_f0a3d_row5_col6\" class=\"data row5 col6\" >0.5358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_f0a3d_row6_col0\" class=\"data row6 col0\" >0.7646</td>\n",
       "      <td id=\"T_f0a3d_row6_col1\" class=\"data row6 col1\" >0.8432</td>\n",
       "      <td id=\"T_f0a3d_row6_col2\" class=\"data row6 col2\" >0.7227</td>\n",
       "      <td id=\"T_f0a3d_row6_col3\" class=\"data row6 col3\" >0.7889</td>\n",
       "      <td id=\"T_f0a3d_row6_col4\" class=\"data row6 col4\" >0.7543</td>\n",
       "      <td id=\"T_f0a3d_row6_col5\" class=\"data row6 col5\" >0.5292</td>\n",
       "      <td id=\"T_f0a3d_row6_col6\" class=\"data row6 col6\" >0.5311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_f0a3d_row7_col0\" class=\"data row7 col0\" >0.7664</td>\n",
       "      <td id=\"T_f0a3d_row7_col1\" class=\"data row7 col1\" >0.8569</td>\n",
       "      <td id=\"T_f0a3d_row7_col2\" class=\"data row7 col2\" >0.7316</td>\n",
       "      <td id=\"T_f0a3d_row7_col3\" class=\"data row7 col3\" >0.7863</td>\n",
       "      <td id=\"T_f0a3d_row7_col4\" class=\"data row7 col4\" >0.7580</td>\n",
       "      <td id=\"T_f0a3d_row7_col5\" class=\"data row7 col5\" >0.5327</td>\n",
       "      <td id=\"T_f0a3d_row7_col6\" class=\"data row7 col6\" >0.5340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_f0a3d_row8_col0\" class=\"data row8 col0\" >0.7593</td>\n",
       "      <td id=\"T_f0a3d_row8_col1\" class=\"data row8 col1\" >0.8388</td>\n",
       "      <td id=\"T_f0a3d_row8_col2\" class=\"data row8 col2\" >0.7090</td>\n",
       "      <td id=\"T_f0a3d_row8_col3\" class=\"data row8 col3\" >0.7884</td>\n",
       "      <td id=\"T_f0a3d_row8_col4\" class=\"data row8 col4\" >0.7466</td>\n",
       "      <td id=\"T_f0a3d_row8_col5\" class=\"data row8 col5\" >0.5187</td>\n",
       "      <td id=\"T_f0a3d_row8_col6\" class=\"data row8 col6\" >0.5213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_f0a3d_row9_col0\" class=\"data row9 col0\" >0.7646</td>\n",
       "      <td id=\"T_f0a3d_row9_col1\" class=\"data row9 col1\" >0.8508</td>\n",
       "      <td id=\"T_f0a3d_row9_col2\" class=\"data row9 col2\" >0.7383</td>\n",
       "      <td id=\"T_f0a3d_row9_col3\" class=\"data row9 col3\" >0.7794</td>\n",
       "      <td id=\"T_f0a3d_row9_col4\" class=\"data row9 col4\" >0.7583</td>\n",
       "      <td id=\"T_f0a3d_row9_col5\" class=\"data row9 col5\" >0.5292</td>\n",
       "      <td id=\"T_f0a3d_row9_col6\" class=\"data row9 col6\" >0.5299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_f0a3d_row10_col0\" class=\"data row10 col0\" >0.7547</td>\n",
       "      <td id=\"T_f0a3d_row10_col1\" class=\"data row10 col1\" >0.8345</td>\n",
       "      <td id=\"T_f0a3d_row10_col2\" class=\"data row10 col2\" >0.7082</td>\n",
       "      <td id=\"T_f0a3d_row10_col3\" class=\"data row10 col3\" >0.7803</td>\n",
       "      <td id=\"T_f0a3d_row10_col4\" class=\"data row10 col4\" >0.7422</td>\n",
       "      <td id=\"T_f0a3d_row10_col5\" class=\"data row10 col5\" >0.5094</td>\n",
       "      <td id=\"T_f0a3d_row10_col6\" class=\"data row10 col6\" >0.5118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f0a3d_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_f0a3d_row11_col0\" class=\"data row11 col0\" >0.0226</td>\n",
       "      <td id=\"T_f0a3d_row11_col1\" class=\"data row11 col1\" >0.0345</td>\n",
       "      <td id=\"T_f0a3d_row11_col2\" class=\"data row11 col2\" >0.0396</td>\n",
       "      <td id=\"T_f0a3d_row11_col3\" class=\"data row11 col3\" >0.0171</td>\n",
       "      <td id=\"T_f0a3d_row11_col4\" class=\"data row11 col4\" >0.0293</td>\n",
       "      <td id=\"T_f0a3d_row11_col5\" class=\"data row11 col5\" >0.0452</td>\n",
       "      <td id=\"T_f0a3d_row11_col6\" class=\"data row11 col6\" >0.0436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1aeef6ca2e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffc5f6557ce549fab293eb04dfee802a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr')\n",
    "\n",
    "evaluate_model(lr,use_train_data=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(lr, plot='auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2dce4_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2dce4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_2dce4_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_2dce4_row0_col1\" class=\"data row0 col1\" >0.7753</td>\n",
       "      <td id=\"T_2dce4_row0_col2\" class=\"data row0 col2\" >0.7616</td>\n",
       "      <td id=\"T_2dce4_row0_col3\" class=\"data row0 col3\" >0.6131</td>\n",
       "      <td id=\"T_2dce4_row0_col4\" class=\"data row0 col4\" >0.2785</td>\n",
       "      <td id=\"T_2dce4_row0_col5\" class=\"data row0 col5\" >0.3830</td>\n",
       "      <td id=\"T_2dce4_row0_col6\" class=\"data row0 col6\" >0.2686</td>\n",
       "      <td id=\"T_2dce4_row0_col7\" class=\"data row0 col7\" >0.2999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1aeef687490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.54816987 0.45183013]\n",
      " [0.20329799 0.79670201]\n",
      " [0.81318773 0.18681227]\n",
      " ...\n",
      " [0.95286122 0.04713878]\n",
      " [0.39809806 0.60190194]\n",
      " [0.52275966 0.47724034]]\n"
     ]
    }
   ],
   "source": [
    "lr_holdout_pred = predict_model(lr)\n",
    "\n",
    "lr_proba = lr.predict_proba(s.X_test_transformed)\n",
    "print(lr_proba)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c8ab1_row10_col0, #T_c8ab1_row10_col1, #T_c8ab1_row10_col2, #T_c8ab1_row10_col3, #T_c8ab1_row10_col4, #T_c8ab1_row10_col5, #T_c8ab1_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c8ab1_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c8ab1_row0_col0\" class=\"data row0 col0\" >0.7078</td>\n",
       "      <td id=\"T_c8ab1_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row0_col2\" class=\"data row0 col2\" >0.5909</td>\n",
       "      <td id=\"T_c8ab1_row0_col3\" class=\"data row0 col3\" >0.7710</td>\n",
       "      <td id=\"T_c8ab1_row0_col4\" class=\"data row0 col4\" >0.6690</td>\n",
       "      <td id=\"T_c8ab1_row0_col5\" class=\"data row0 col5\" >0.4155</td>\n",
       "      <td id=\"T_c8ab1_row0_col6\" class=\"data row0 col6\" >0.4273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_c8ab1_row1_col0\" class=\"data row1 col0\" >0.7462</td>\n",
       "      <td id=\"T_c8ab1_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row1_col2\" class=\"data row1 col2\" >0.6475</td>\n",
       "      <td id=\"T_c8ab1_row1_col3\" class=\"data row1 col3\" >0.8067</td>\n",
       "      <td id=\"T_c8ab1_row1_col4\" class=\"data row1 col4\" >0.7184</td>\n",
       "      <td id=\"T_c8ab1_row1_col5\" class=\"data row1 col5\" >0.4925</td>\n",
       "      <td id=\"T_c8ab1_row1_col6\" class=\"data row1 col6\" >0.5023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_c8ab1_row2_col0\" class=\"data row2 col0\" >0.7636</td>\n",
       "      <td id=\"T_c8ab1_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row2_col2\" class=\"data row2 col2\" >0.6553</td>\n",
       "      <td id=\"T_c8ab1_row2_col3\" class=\"data row2 col3\" >0.8364</td>\n",
       "      <td id=\"T_c8ab1_row2_col4\" class=\"data row2 col4\" >0.7349</td>\n",
       "      <td id=\"T_c8ab1_row2_col5\" class=\"data row2 col5\" >0.5272</td>\n",
       "      <td id=\"T_c8ab1_row2_col6\" class=\"data row2 col6\" >0.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_c8ab1_row3_col0\" class=\"data row3 col0\" >0.7601</td>\n",
       "      <td id=\"T_c8ab1_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row3_col2\" class=\"data row3 col2\" >0.6866</td>\n",
       "      <td id=\"T_c8ab1_row3_col3\" class=\"data row3 col3\" >0.8049</td>\n",
       "      <td id=\"T_c8ab1_row3_col4\" class=\"data row3 col4\" >0.7410</td>\n",
       "      <td id=\"T_c8ab1_row3_col5\" class=\"data row3 col5\" >0.5202</td>\n",
       "      <td id=\"T_c8ab1_row3_col6\" class=\"data row3 col6\" >0.5259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_c8ab1_row4_col0\" class=\"data row4 col0\" >0.7601</td>\n",
       "      <td id=\"T_c8ab1_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row4_col2\" class=\"data row4 col2\" >0.6741</td>\n",
       "      <td id=\"T_c8ab1_row4_col3\" class=\"data row4 col3\" >0.8141</td>\n",
       "      <td id=\"T_c8ab1_row4_col4\" class=\"data row4 col4\" >0.7375</td>\n",
       "      <td id=\"T_c8ab1_row4_col5\" class=\"data row4 col5\" >0.5202</td>\n",
       "      <td id=\"T_c8ab1_row4_col6\" class=\"data row4 col6\" >0.5281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_c8ab1_row5_col0\" class=\"data row5 col0\" >0.7556</td>\n",
       "      <td id=\"T_c8ab1_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row5_col2\" class=\"data row5 col2\" >0.6875</td>\n",
       "      <td id=\"T_c8ab1_row5_col3\" class=\"data row5 col3\" >0.7960</td>\n",
       "      <td id=\"T_c8ab1_row5_col4\" class=\"data row5 col4\" >0.7378</td>\n",
       "      <td id=\"T_c8ab1_row5_col5\" class=\"data row5 col5\" >0.5112</td>\n",
       "      <td id=\"T_c8ab1_row5_col6\" class=\"data row5 col6\" >0.5161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_c8ab1_row6_col0\" class=\"data row6 col0\" >0.7503</td>\n",
       "      <td id=\"T_c8ab1_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row6_col2\" class=\"data row6 col2\" >0.6723</td>\n",
       "      <td id=\"T_c8ab1_row6_col3\" class=\"data row6 col3\" >0.7968</td>\n",
       "      <td id=\"T_c8ab1_row6_col4\" class=\"data row6 col4\" >0.7292</td>\n",
       "      <td id=\"T_c8ab1_row6_col5\" class=\"data row6 col5\" >0.5007</td>\n",
       "      <td id=\"T_c8ab1_row6_col6\" class=\"data row6 col6\" >0.5069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_c8ab1_row7_col0\" class=\"data row7 col0\" >0.7468</td>\n",
       "      <td id=\"T_c8ab1_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row7_col2\" class=\"data row7 col2\" >0.6480</td>\n",
       "      <td id=\"T_c8ab1_row7_col3\" class=\"data row7 col3\" >0.8077</td>\n",
       "      <td id=\"T_c8ab1_row7_col4\" class=\"data row7 col4\" >0.7191</td>\n",
       "      <td id=\"T_c8ab1_row7_col5\" class=\"data row7 col5\" >0.4937</td>\n",
       "      <td id=\"T_c8ab1_row7_col6\" class=\"data row7 col6\" >0.5036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_c8ab1_row8_col0\" class=\"data row8 col0\" >0.7537</td>\n",
       "      <td id=\"T_c8ab1_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row8_col2\" class=\"data row8 col2\" >0.6949</td>\n",
       "      <td id=\"T_c8ab1_row8_col3\" class=\"data row8 col3\" >0.7875</td>\n",
       "      <td id=\"T_c8ab1_row8_col4\" class=\"data row8 col4\" >0.7383</td>\n",
       "      <td id=\"T_c8ab1_row8_col5\" class=\"data row8 col5\" >0.5073</td>\n",
       "      <td id=\"T_c8ab1_row8_col6\" class=\"data row8 col6\" >0.5109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_c8ab1_row9_col0\" class=\"data row9 col0\" >0.7593</td>\n",
       "      <td id=\"T_c8ab1_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row9_col2\" class=\"data row9 col2\" >0.6828</td>\n",
       "      <td id=\"T_c8ab1_row9_col3\" class=\"data row9 col3\" >0.8063</td>\n",
       "      <td id=\"T_c8ab1_row9_col4\" class=\"data row9 col4\" >0.7394</td>\n",
       "      <td id=\"T_c8ab1_row9_col5\" class=\"data row9 col5\" >0.5187</td>\n",
       "      <td id=\"T_c8ab1_row9_col6\" class=\"data row9 col6\" >0.5249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_c8ab1_row10_col0\" class=\"data row10 col0\" >0.7504</td>\n",
       "      <td id=\"T_c8ab1_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row10_col2\" class=\"data row10 col2\" >0.6640</td>\n",
       "      <td id=\"T_c8ab1_row10_col3\" class=\"data row10 col3\" >0.8027</td>\n",
       "      <td id=\"T_c8ab1_row10_col4\" class=\"data row10 col4\" >0.7265</td>\n",
       "      <td id=\"T_c8ab1_row10_col5\" class=\"data row10 col5\" >0.5007</td>\n",
       "      <td id=\"T_c8ab1_row10_col6\" class=\"data row10 col6\" >0.5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8ab1_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_c8ab1_row11_col0\" class=\"data row11 col0\" >0.0153</td>\n",
       "      <td id=\"T_c8ab1_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
       "      <td id=\"T_c8ab1_row11_col2\" class=\"data row11 col2\" >0.0291</td>\n",
       "      <td id=\"T_c8ab1_row11_col3\" class=\"data row11 col3\" >0.0162</td>\n",
       "      <td id=\"T_c8ab1_row11_col4\" class=\"data row11 col4\" >0.0207</td>\n",
       "      <td id=\"T_c8ab1_row11_col5\" class=\"data row11 col5\" >0.0305</td>\n",
       "      <td id=\"T_c8ab1_row11_col6\" class=\"data row11 col6\" >0.0294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e4a6a7f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [],
      "text/plain": [
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     "output_type": "display_data"
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    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_00485_row10_col0, #T_00485_row10_col1, #T_00485_row10_col2, #T_00485_row10_col3, #T_00485_row10_col4, #T_00485_row10_col5, #T_00485_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_00485_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_00485_row0_col0\" class=\"data row0 col0\" >0.6939</td>\n",
       "      <td id=\"T_00485_row0_col1\" class=\"data row0 col1\" >0.7474</td>\n",
       "      <td id=\"T_00485_row0_col2\" class=\"data row0 col2\" >0.6166</td>\n",
       "      <td id=\"T_00485_row0_col3\" class=\"data row0 col3\" >0.7292</td>\n",
       "      <td id=\"T_00485_row0_col4\" class=\"data row0 col4\" >0.6682</td>\n",
       "      <td id=\"T_00485_row0_col5\" class=\"data row0 col5\" >0.3878</td>\n",
       "      <td id=\"T_00485_row0_col6\" class=\"data row0 col6\" >0.3925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_00485_row1_col0\" class=\"data row1 col0\" >0.7400</td>\n",
       "      <td id=\"T_00485_row1_col1\" class=\"data row1 col1\" >0.8193</td>\n",
       "      <td id=\"T_00485_row1_col2\" class=\"data row1 col2\" >0.6753</td>\n",
       "      <td id=\"T_00485_row1_col3\" class=\"data row1 col3\" >0.7756</td>\n",
       "      <td id=\"T_00485_row1_col4\" class=\"data row1 col4\" >0.7220</td>\n",
       "      <td id=\"T_00485_row1_col5\" class=\"data row1 col5\" >0.4800</td>\n",
       "      <td id=\"T_00485_row1_col6\" class=\"data row1 col6\" >0.4840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_00485_row2_col0\" class=\"data row2 col0\" >0.7611</td>\n",
       "      <td id=\"T_00485_row2_col1\" class=\"data row2 col1\" >0.8411</td>\n",
       "      <td id=\"T_00485_row2_col2\" class=\"data row2 col2\" >0.6987</td>\n",
       "      <td id=\"T_00485_row2_col3\" class=\"data row2 col3\" >0.7982</td>\n",
       "      <td id=\"T_00485_row2_col4\" class=\"data row2 col4\" >0.7452</td>\n",
       "      <td id=\"T_00485_row2_col5\" class=\"data row2 col5\" >0.5222</td>\n",
       "      <td id=\"T_00485_row2_col6\" class=\"data row2 col6\" >0.5263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_00485_row3_col0\" class=\"data row3 col0\" >0.7584</td>\n",
       "      <td id=\"T_00485_row3_col1\" class=\"data row3 col1\" >0.8501</td>\n",
       "      <td id=\"T_00485_row3_col2\" class=\"data row3 col2\" >0.6948</td>\n",
       "      <td id=\"T_00485_row3_col3\" class=\"data row3 col3\" >0.7959</td>\n",
       "      <td id=\"T_00485_row3_col4\" class=\"data row3 col4\" >0.7419</td>\n",
       "      <td id=\"T_00485_row3_col5\" class=\"data row3 col5\" >0.5167</td>\n",
       "      <td id=\"T_00485_row3_col6\" class=\"data row3 col6\" >0.5209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_00485_row4_col0\" class=\"data row4 col0\" >0.7539</td>\n",
       "      <td id=\"T_00485_row4_col1\" class=\"data row4 col1\" >0.8413</td>\n",
       "      <td id=\"T_00485_row4_col2\" class=\"data row4 col2\" >0.6905</td>\n",
       "      <td id=\"T_00485_row4_col3\" class=\"data row4 col3\" >0.7906</td>\n",
       "      <td id=\"T_00485_row4_col4\" class=\"data row4 col4\" >0.7372</td>\n",
       "      <td id=\"T_00485_row4_col5\" class=\"data row4 col5\" >0.5077</td>\n",
       "      <td id=\"T_00485_row4_col6\" class=\"data row4 col6\" >0.5118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_00485_row5_col0\" class=\"data row5 col0\" >0.7539</td>\n",
       "      <td id=\"T_00485_row5_col1\" class=\"data row5 col1\" >0.8383</td>\n",
       "      <td id=\"T_00485_row5_col2\" class=\"data row5 col2\" >0.6887</td>\n",
       "      <td id=\"T_00485_row5_col3\" class=\"data row5 col3\" >0.7920</td>\n",
       "      <td id=\"T_00485_row5_col4\" class=\"data row5 col4\" >0.7367</td>\n",
       "      <td id=\"T_00485_row5_col5\" class=\"data row5 col5\" >0.5077</td>\n",
       "      <td id=\"T_00485_row5_col6\" class=\"data row5 col6\" >0.5121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_00485_row6_col0\" class=\"data row6 col0\" >0.7511</td>\n",
       "      <td id=\"T_00485_row6_col1\" class=\"data row6 col1\" >0.8361</td>\n",
       "      <td id=\"T_00485_row6_col2\" class=\"data row6 col2\" >0.6883</td>\n",
       "      <td id=\"T_00485_row6_col3\" class=\"data row6 col3\" >0.7873</td>\n",
       "      <td id=\"T_00485_row6_col4\" class=\"data row6 col4\" >0.7345</td>\n",
       "      <td id=\"T_00485_row6_col5\" class=\"data row6 col5\" >0.5023</td>\n",
       "      <td id=\"T_00485_row6_col6\" class=\"data row6 col6\" >0.5063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_00485_row7_col0\" class=\"data row7 col0\" >0.7453</td>\n",
       "      <td id=\"T_00485_row7_col1\" class=\"data row7 col1\" >0.8360</td>\n",
       "      <td id=\"T_00485_row7_col2\" class=\"data row7 col2\" >0.6648</td>\n",
       "      <td id=\"T_00485_row7_col3\" class=\"data row7 col3\" >0.7924</td>\n",
       "      <td id=\"T_00485_row7_col4\" class=\"data row7 col4\" >0.7230</td>\n",
       "      <td id=\"T_00485_row7_col5\" class=\"data row7 col5\" >0.4905</td>\n",
       "      <td id=\"T_00485_row7_col6\" class=\"data row7 col6\" >0.4970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_00485_row8_col0\" class=\"data row8 col0\" >0.7519</td>\n",
       "      <td id=\"T_00485_row8_col1\" class=\"data row8 col1\" >0.8367</td>\n",
       "      <td id=\"T_00485_row8_col2\" class=\"data row8 col2\" >0.7000</td>\n",
       "      <td id=\"T_00485_row8_col3\" class=\"data row8 col3\" >0.7812</td>\n",
       "      <td id=\"T_00485_row8_col4\" class=\"data row8 col4\" >0.7384</td>\n",
       "      <td id=\"T_00485_row8_col5\" class=\"data row8 col5\" >0.5038</td>\n",
       "      <td id=\"T_00485_row8_col6\" class=\"data row8 col6\" >0.5066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_00485_row9_col0\" class=\"data row9 col0\" >0.7554</td>\n",
       "      <td id=\"T_00485_row9_col1\" class=\"data row9 col1\" >0.8370</td>\n",
       "      <td id=\"T_00485_row9_col2\" class=\"data row9 col2\" >0.6832</td>\n",
       "      <td id=\"T_00485_row9_col3\" class=\"data row9 col3\" >0.7986</td>\n",
       "      <td id=\"T_00485_row9_col4\" class=\"data row9 col4\" >0.7364</td>\n",
       "      <td id=\"T_00485_row9_col5\" class=\"data row9 col5\" >0.5109</td>\n",
       "      <td id=\"T_00485_row9_col6\" class=\"data row9 col6\" >0.5163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_00485_row10_col0\" class=\"data row10 col0\" >0.7465</td>\n",
       "      <td id=\"T_00485_row10_col1\" class=\"data row10 col1\" >0.8283</td>\n",
       "      <td id=\"T_00485_row10_col2\" class=\"data row10 col2\" >0.6801</td>\n",
       "      <td id=\"T_00485_row10_col3\" class=\"data row10 col3\" >0.7841</td>\n",
       "      <td id=\"T_00485_row10_col4\" class=\"data row10 col4\" >0.7283</td>\n",
       "      <td id=\"T_00485_row10_col5\" class=\"data row10 col5\" >0.4929</td>\n",
       "      <td id=\"T_00485_row10_col6\" class=\"data row10 col6\" >0.4974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00485_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_00485_row11_col0\" class=\"data row11 col0\" >0.0184</td>\n",
       "      <td id=\"T_00485_row11_col1\" class=\"data row11 col1\" >0.0279</td>\n",
       "      <td id=\"T_00485_row11_col2\" class=\"data row11 col2\" >0.0235</td>\n",
       "      <td id=\"T_00485_row11_col3\" class=\"data row11 col3\" >0.0196</td>\n",
       "      <td id=\"T_00485_row11_col4\" class=\"data row11 col4\" >0.0212</td>\n",
       "      <td id=\"T_00485_row11_col5\" class=\"data row11 col5\" >0.0369</td>\n",
       "      <td id=\"T_00485_row11_col6\" class=\"data row11 col6\" >0.0368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1e49cfd70d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "792ca5ae6205418eb90d296cabd7f8ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm = create_model('svm')\n",
    "\n",
    "calibrated_svm = calibrate_model(svm, calibrate_fold=5)\n",
    "evaluate_model(svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_holdout_pred = predict_model(svm)\n",
    "svm_calibrated_holdout_pred = predict_model(calibrated_svm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a17f8_row10_col0, #T_a17f8_row10_col1, #T_a17f8_row10_col2, #T_a17f8_row10_col3, #T_a17f8_row10_col4, #T_a17f8_row10_col5, #T_a17f8_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a17f8_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a17f8_row0_col0\" class=\"data row0 col0\" >0.7058</td>\n",
       "      <td id=\"T_a17f8_row0_col1\" class=\"data row0 col1\" >0.7700</td>\n",
       "      <td id=\"T_a17f8_row0_col2\" class=\"data row0 col2\" >0.6635</td>\n",
       "      <td id=\"T_a17f8_row0_col3\" class=\"data row0 col3\" >0.7247</td>\n",
       "      <td id=\"T_a17f8_row0_col4\" class=\"data row0 col4\" >0.6928</td>\n",
       "      <td id=\"T_a17f8_row0_col5\" class=\"data row0 col5\" >0.4116</td>\n",
       "      <td id=\"T_a17f8_row0_col6\" class=\"data row0 col6\" >0.4131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a17f8_row1_col0\" class=\"data row1 col0\" >0.7308</td>\n",
       "      <td id=\"T_a17f8_row1_col1\" class=\"data row1 col1\" >0.8069</td>\n",
       "      <td id=\"T_a17f8_row1_col2\" class=\"data row1 col2\" >0.6862</td>\n",
       "      <td id=\"T_a17f8_row1_col3\" class=\"data row1 col3\" >0.7533</td>\n",
       "      <td id=\"T_a17f8_row1_col4\" class=\"data row1 col4\" >0.7182</td>\n",
       "      <td id=\"T_a17f8_row1_col5\" class=\"data row1 col5\" >0.4616</td>\n",
       "      <td id=\"T_a17f8_row1_col6\" class=\"data row1 col6\" >0.4634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a17f8_row2_col0\" class=\"data row2 col0\" >0.7554</td>\n",
       "      <td id=\"T_a17f8_row2_col1\" class=\"data row2 col1\" >0.8302</td>\n",
       "      <td id=\"T_a17f8_row2_col2\" class=\"data row2 col2\" >0.7179</td>\n",
       "      <td id=\"T_a17f8_row2_col3\" class=\"data row2 col3\" >0.7761</td>\n",
       "      <td id=\"T_a17f8_row2_col4\" class=\"data row2 col4\" >0.7458</td>\n",
       "      <td id=\"T_a17f8_row2_col5\" class=\"data row2 col5\" >0.5108</td>\n",
       "      <td id=\"T_a17f8_row2_col6\" class=\"data row2 col6\" >0.5123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_a17f8_row3_col0\" class=\"data row3 col0\" >0.7537</td>\n",
       "      <td id=\"T_a17f8_row3_col1\" class=\"data row3 col1\" >0.8285</td>\n",
       "      <td id=\"T_a17f8_row3_col2\" class=\"data row3 col2\" >0.7163</td>\n",
       "      <td id=\"T_a17f8_row3_col3\" class=\"data row3 col3\" >0.7741</td>\n",
       "      <td id=\"T_a17f8_row3_col4\" class=\"data row3 col4\" >0.7441</td>\n",
       "      <td id=\"T_a17f8_row3_col5\" class=\"data row3 col5\" >0.5073</td>\n",
       "      <td id=\"T_a17f8_row3_col6\" class=\"data row3 col6\" >0.5087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_a17f8_row4_col0\" class=\"data row4 col0\" >0.7480</td>\n",
       "      <td id=\"T_a17f8_row4_col1\" class=\"data row4 col1\" >0.8177</td>\n",
       "      <td id=\"T_a17f8_row4_col2\" class=\"data row4 col2\" >0.7276</td>\n",
       "      <td id=\"T_a17f8_row4_col3\" class=\"data row4 col3\" >0.7585</td>\n",
       "      <td id=\"T_a17f8_row4_col4\" class=\"data row4 col4\" >0.7427</td>\n",
       "      <td id=\"T_a17f8_row4_col5\" class=\"data row4 col5\" >0.4960</td>\n",
       "      <td id=\"T_a17f8_row4_col6\" class=\"data row4 col6\" >0.4964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_a17f8_row5_col0\" class=\"data row5 col0\" >0.7498</td>\n",
       "      <td id=\"T_a17f8_row5_col1\" class=\"data row5 col1\" >0.8230</td>\n",
       "      <td id=\"T_a17f8_row5_col2\" class=\"data row5 col2\" >0.7055</td>\n",
       "      <td id=\"T_a17f8_row5_col3\" class=\"data row5 col3\" >0.7741</td>\n",
       "      <td id=\"T_a17f8_row5_col4\" class=\"data row5 col4\" >0.7382</td>\n",
       "      <td id=\"T_a17f8_row5_col5\" class=\"data row5 col5\" >0.4995</td>\n",
       "      <td id=\"T_a17f8_row5_col6\" class=\"data row5 col6\" >0.5015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_a17f8_row6_col0\" class=\"data row6 col0\" >0.7445</td>\n",
       "      <td id=\"T_a17f8_row6_col1\" class=\"data row6 col1\" >0.8175</td>\n",
       "      <td id=\"T_a17f8_row6_col2\" class=\"data row6 col2\" >0.7301</td>\n",
       "      <td id=\"T_a17f8_row6_col3\" class=\"data row6 col3\" >0.7518</td>\n",
       "      <td id=\"T_a17f8_row6_col4\" class=\"data row6 col4\" >0.7408</td>\n",
       "      <td id=\"T_a17f8_row6_col5\" class=\"data row6 col5\" >0.4890</td>\n",
       "      <td id=\"T_a17f8_row6_col6\" class=\"data row6 col6\" >0.4892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_a17f8_row7_col0\" class=\"data row7 col0\" >0.7591</td>\n",
       "      <td id=\"T_a17f8_row7_col1\" class=\"data row7 col1\" >0.8375</td>\n",
       "      <td id=\"T_a17f8_row7_col2\" class=\"data row7 col2\" >0.7270</td>\n",
       "      <td id=\"T_a17f8_row7_col3\" class=\"data row7 col3\" >0.7770</td>\n",
       "      <td id=\"T_a17f8_row7_col4\" class=\"data row7 col4\" >0.7512</td>\n",
       "      <td id=\"T_a17f8_row7_col5\" class=\"data row7 col5\" >0.5183</td>\n",
       "      <td id=\"T_a17f8_row7_col6\" class=\"data row7 col6\" >0.5194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_a17f8_row8_col0\" class=\"data row8 col0\" >0.7419</td>\n",
       "      <td id=\"T_a17f8_row8_col1\" class=\"data row8 col1\" >0.8142</td>\n",
       "      <td id=\"T_a17f8_row8_col2\" class=\"data row8 col2\" >0.7125</td>\n",
       "      <td id=\"T_a17f8_row8_col3\" class=\"data row8 col3\" >0.7572</td>\n",
       "      <td id=\"T_a17f8_row8_col4\" class=\"data row8 col4\" >0.7342</td>\n",
       "      <td id=\"T_a17f8_row8_col5\" class=\"data row8 col5\" >0.4839</td>\n",
       "      <td id=\"T_a17f8_row8_col6\" class=\"data row8 col6\" >0.4847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_a17f8_row9_col0\" class=\"data row9 col0\" >0.7640</td>\n",
       "      <td id=\"T_a17f8_row9_col1\" class=\"data row9 col1\" >0.8289</td>\n",
       "      <td id=\"T_a17f8_row9_col2\" class=\"data row9 col2\" >0.7371</td>\n",
       "      <td id=\"T_a17f8_row9_col3\" class=\"data row9 col3\" >0.7791</td>\n",
       "      <td id=\"T_a17f8_row9_col4\" class=\"data row9 col4\" >0.7575</td>\n",
       "      <td id=\"T_a17f8_row9_col5\" class=\"data row9 col5\" >0.5280</td>\n",
       "      <td id=\"T_a17f8_row9_col6\" class=\"data row9 col6\" >0.5288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_a17f8_row10_col0\" class=\"data row10 col0\" >0.7453</td>\n",
       "      <td id=\"T_a17f8_row10_col1\" class=\"data row10 col1\" >0.8174</td>\n",
       "      <td id=\"T_a17f8_row10_col2\" class=\"data row10 col2\" >0.7124</td>\n",
       "      <td id=\"T_a17f8_row10_col3\" class=\"data row10 col3\" >0.7626</td>\n",
       "      <td id=\"T_a17f8_row10_col4\" class=\"data row10 col4\" >0.7365</td>\n",
       "      <td id=\"T_a17f8_row10_col5\" class=\"data row10 col5\" >0.4906</td>\n",
       "      <td id=\"T_a17f8_row10_col6\" class=\"data row10 col6\" >0.4917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a17f8_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_a17f8_row11_col0\" class=\"data row11 col0\" >0.0159</td>\n",
       "      <td id=\"T_a17f8_row11_col1\" class=\"data row11 col1\" >0.0180</td>\n",
       "      <td id=\"T_a17f8_row11_col2\" class=\"data row11 col2\" >0.0213</td>\n",
       "      <td id=\"T_a17f8_row11_col3\" class=\"data row11 col3\" >0.0162</td>\n",
       "      <td id=\"T_a17f8_row11_col4\" class=\"data row11 col4\" >0.0176</td>\n",
       "      <td id=\"T_a17f8_row11_col5\" class=\"data row11 col5\" >0.0318</td>\n",
       "      <td id=\"T_a17f8_row11_col6\" class=\"data row11 col6\" >0.0316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd77e1ece0e4438e8bcbfb323ff10abc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Creating Parameter Space\n",
    "n_estimators = [25] # not a good hyperparameter to tune\n",
    "max_depth = [2, 5, 8, 15]\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [2,3,4,5]\n",
    "criterion = ['gini', 'entropy']\n",
    "\n",
    "parameters = {\n",
    "            \"n_estimators\": n_estimators,\n",
    "            \"max_depth\": max_depth,\n",
    "            \"min_samples_split\": min_samples_split,\n",
    "            \"min_samples_leaf\": min_samples_leaf,\n",
    "            \"criterion\": criterion}\n",
    "\n",
    "\n",
    "clf = RandomForestClassifier(random_state=420, n_jobs=-1,n_estimators=100, max_depth=5, min_samples_split=0.2 )\n",
    "\n",
    "rf = create_model(clf)\n",
    "\n",
    "#tuned_rf = tune_model(rf, n_iter=5, custom_grid = parameters)\n",
    "\n",
    "evaluate_model(rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_holdout_pred = predict_model(rf)\n",
    "rf_calibrated_holdout_pred = predict_model(calibrated_rf)\n",
    "\n",
    "rf_proba = rf.predict_proba(s.X_test_transformed)\n",
    "print(rf_proba)\n",
    "rf_calibrated_proba = calibrated_rf.predict_proba(s.X_test_transformed)\n",
    "print(rf_calibrated_proba)\n",
    "\n",
    "plot_model(rf, plot = 'calibration')\n",
    "plot_model(calibrated_rf, plot = 'calibration')"
   ]
  }
 ],
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