Image_Classification_HOG_2_roc_auc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import datetime\n",
    "import pickle\n",
    "import numpy as np\n",
    "from sklearn import svm, metrics\n",
    "from sklearn.model_selection import StratifiedKFold,GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Dataset from the pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time:  2019-01-16 11:31:07.510567\n"
     ]
    }
   ],
   "source": [
    "# Start point\n",
    "start_time = time.time()\n",
    "print(\"Start time: \", datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle_in = open(\"Signature_Dataset_HOG_110_2_int.pickle\",\"rb\")\n",
    "image_dataset = pickle.load(pickle_in)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split data in Training Set and Testing Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Dataset Loaded From Pickle----\n"
     ]
    }
   ],
   "source": [
    "X_train = image_dataset.X_train\n",
    "X_test = image_dataset.X_test\n",
    "y_train = image_dataset.y_train\n",
    "y_test = image_dataset.y_test\n",
    "\n",
    "print(\"----Dataset Loaded From Pickle----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train data with parameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dictionary of models and hyperparameters\n",
    "\n",
    "models_and_hyperparameters={'LR':(LogisticRegression(),{'C': [0.01, 0.05, 0.1, 0.5, 1, 2],\n",
    "                                                        'penalty': ['l1', 'l2'], \n",
    "                                                        'class_weight': ['balanced']}),\n",
    "                           'SVM':(svm.SVC(),{'C': [1, 10, 100, 1000],\n",
    "                                             'gamma': [0.001, 0.0001],\n",
    "                                             'kernel': ['rbf'],\n",
    "                                             'class_weight': ['balanced']}),\n",
    "                           'RFC':(RandomForestClassifier(), {'n_estimators': [50,100,200],\n",
    "                                                             # 'max_features': ['auto', 'sqrt'],\n",
    "                                                             'max_depth': [50,60,70],\n",
    "                                                             'min_samples_split': [2,5,10],\n",
    "                                                             #'min_samples_leaf': [1,2],\n",
    "                                                             'bootstrap': [True, False],\n",
    "                                                             'class_weight': ['balanced']})\n",
    "                           }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training----\n",
      "Fitting 10 folds for each of 12 candidates, totalling 120 fits\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.7s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.8s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.7s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.7s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.7s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.7090909090909091, total=   1.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.7094582185491276, total=   1.9s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6968778696051423, total=   1.8s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.7122130394857668, total=   1.8s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6885215794306703, total=   1.8s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.716712580348944, total=   1.8s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.7226813590449954, total=   1.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6955004591368227, total=   1.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.686317722681359, total=   1.8s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.721028466483012, total=   1.6s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.6088154269972452, total=   0.8s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.5415059687786961, total=   0.9s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.5959595959595959, total=   0.9s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.5857667584940311, total=   0.8s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.49765840220385676, total=   0.8s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.604775022956841, total=   0.9s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.6447199265381083, total=   0.8s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.6035812672176308, total=   0.8s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.5112029384756658, total=   0.8s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.6224977043158861, total=   0.8s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.7213957759412304, total=   2.5s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.7395775941230487, total=   2.5s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.6922865013774104, total=   2.6s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.7308539944903581, total=   2.6s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.7499540863177228, total=   2.5s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.756932966023875, total=   2.5s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.7378328741965106, total=   2.4s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.7021120293847566, total=   2.5s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.7090909090909091, total=   2.5s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.732415059687787, total=   2.5s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.7041322314049586, total=   1.0s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.636179981634527, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.6352617079889807, total=   1.0s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.655463728191001, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5601469237832875, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.6641873278236915, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.6720844811753902, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.7064279155188247, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.621028466483012, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.6878787878787879, total=   0.9s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7214876033057851, total=   3.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7403122130394858, total=   3.4s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.6898989898989899, total=   3.4s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7330578512396695, total=   3.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7573921028466484, total=   3.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7607897153351698, total=   3.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7398530762167127, total=   3.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.698714416896235, total=   3.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7101928374655647, total=   3.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.7338842975206612, total=   3.2s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6837465564738293, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6869605142332416, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6659320477502295, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.7416896235078053, total=   1.2s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6808999081726355, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6717171717171718, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.7216712580348944, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6393021120293848, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6629017447199265, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l1, score=0.6998163452708906, total=   1.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.717263544536272, total=   4.4s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.7346189164370983, total=   3.9s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6837465564738292, total=   3.8s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.7282828282828282, total=   3.9s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.769146005509642, total=   3.8s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.7641873278236914, total=   3.9s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.7404040404040404, total=   3.9s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.692929292929293, total=   3.8s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.7131313131313132, total=   3.8s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.7337924701561065, total=   3.8s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6815426997245179, total=   1.3s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6679522497704317, total=   1.2s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6395775941230487, total=   1.1s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.7434343434343434, total=   1.2s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6912764003673094, total=   1.2s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6804407713498624, total=   1.1s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.7166207529843893, total=   1.1s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6337006427915519, total=   1.2s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6525252525252525, total=   1.1s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6989898989898989, total=   1.2s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7148760330578513, total=   4.8s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7328741965105601, total=   4.7s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6815426997245179, total=   4.8s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7276400367309459, total=   4.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.769880624426079, total=   4.8s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7629935720844812, total=   4.8s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.740495867768595, total=   4.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6905417814508724, total=   4.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7130394857667585, total=   4.7s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7356290174471992, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6900826446280993, total=   1.2s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6517906336088155, total=   1.2s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6308539944903582, total=   1.1s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.7337006427915519, total=   1.1s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6920110192837466, total=   1.2s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6838383838383838, total=   1.1s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.7055096418732782, total=   1.3s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6376492194674013, total=   1.2s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6510560146923783, total=   1.1s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.7064279155188246, total=   1.3s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7143250688705235, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7312213039485767, total=   4.7s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6806244260789717, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7265381083562903, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7699724517906337, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7631772268135903, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7384756657483931, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6892561983471075, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7140495867768596, total=   4.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7353535353535354, total=   4.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 120 out of 120 | elapsed:  4.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Ended----\n"
     ]
    }
   ],
   "source": [
    "#LOGISTIC REGRESSION\n",
    "\n",
    "print(\"----Training----\")\n",
    "\n",
    "k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=31)\n",
    "\n",
    "model = models_and_hyperparameters['LR'][0]\n",
    "hyperparameters = models_and_hyperparameters['LR'][1]\n",
    "\n",
    "gs = GridSearchCV(model, \n",
    "                   param_grid = hyperparameters,\n",
    "                   cv=k_fold, \n",
    "                   scoring='roc_auc',\n",
    "                   verbose=3,\n",
    "                   return_train_score=True)\n",
    "\n",
    "gs.fit(X_train, y_train)\n",
    "\n",
    "print(\"----Training Ended----\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_class_weight</th>\n",
       "      <th>param_penalty</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.677820</td>\n",
       "      <td>0.009528</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 0.01, 'class_weight': 'balanced', 'penal...</td>\n",
       "      <td>12</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.048030</td>\n",
       "      <td>0.001429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.711092</td>\n",
       "      <td>0.008939</td>\n",
       "      <td>0.705840</td>\n",
       "      <td>0.985044</td>\n",
       "      <td>0.01</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 0.01, 'class_weight': 'balanced', 'penal...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.709091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.695500</td>\n",
       "      <td>0.985170</td>\n",
       "      <td>0.686318</td>\n",
       "      <td>0.985660</td>\n",
       "      <td>0.721028</td>\n",
       "      <td>0.985814</td>\n",
       "      <td>0.122934</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.012510</td>\n",
       "      <td>8.120014e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822581</td>\n",
       "      <td>0.008945</td>\n",
       "      <td>0.581648</td>\n",
       "      <td>0.612603</td>\n",
       "      <td>0.05</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 0.05, 'class_weight': 'balanced', 'penal...</td>\n",
       "      <td>11</td>\n",
       "      <td>0.608815</td>\n",
       "      <td>...</td>\n",
       "      <td>0.603581</td>\n",
       "      <td>0.601626</td>\n",
       "      <td>0.511203</td>\n",
       "      <td>0.617794</td>\n",
       "      <td>0.622498</td>\n",
       "      <td>0.596533</td>\n",
       "      <td>0.028789</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.046098</td>\n",
       "      <td>1.033102e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.510007</td>\n",
       "      <td>0.009104</td>\n",
       "      <td>0.727245</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>0.05</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 0.05, 'class_weight': 'balanced', 'penal...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.721396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.702112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.709091</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.732415</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.063607</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.019795</td>\n",
       "      <td>1.855346e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.925438</td>\n",
       "      <td>0.008947</td>\n",
       "      <td>0.654279</td>\n",
       "      <td>0.728097</td>\n",
       "      <td>0.1</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 0.1, 'class_weight': 'balanced', 'penalt...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.704132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.706428</td>\n",
       "      <td>0.726941</td>\n",
       "      <td>0.621028</td>\n",
       "      <td>0.729782</td>\n",
       "      <td>0.687879</td>\n",
       "      <td>0.725248</td>\n",
       "      <td>0.024421</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.041721</td>\n",
       "      <td>4.020678e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.219833</td>\n",
       "      <td>0.009413</td>\n",
       "      <td>0.728558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 0.1, 'class_weight': 'balanced', 'penalt...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.721488</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698714</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.710193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.733884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080889</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.022264</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.090583</td>\n",
       "      <td>0.008933</td>\n",
       "      <td>0.685464</td>\n",
       "      <td>0.996418</td>\n",
       "      <td>0.5</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 0.5, 'class_weight': 'balanced', 'penalt...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.683747</td>\n",
       "      <td>...</td>\n",
       "      <td>0.639302</td>\n",
       "      <td>0.996742</td>\n",
       "      <td>0.662902</td>\n",
       "      <td>0.996333</td>\n",
       "      <td>0.699816</td>\n",
       "      <td>0.996322</td>\n",
       "      <td>0.032679</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.028133</td>\n",
       "      <td>2.730590e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.888121</td>\n",
       "      <td>0.008892</td>\n",
       "      <td>0.727750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 0.5, 'class_weight': 'balanced', 'penalt...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.717264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692929</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.713131</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.733792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171257</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.025996</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.136910</td>\n",
       "      <td>0.008932</td>\n",
       "      <td>0.680606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 1, 'class_weight': 'balanced', 'penalty'...</td>\n",
       "      <td>8</td>\n",
       "      <td>0.681543</td>\n",
       "      <td>...</td>\n",
       "      <td>0.633701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.652525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.698990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058563</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.032449</td>\n",
       "      <td>6.802027e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.715721</td>\n",
       "      <td>0.008895</td>\n",
       "      <td>0.726951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 1, 'class_weight': 'balanced', 'penalty'...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.714876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.690542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.713039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.735629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.072102</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.026764</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.174981</td>\n",
       "      <td>0.008955</td>\n",
       "      <td>0.678292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 2, 'class_weight': 'balanced', 'penalty'...</td>\n",
       "      <td>9</td>\n",
       "      <td>0.690083</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637649</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651056</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.706428</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048649</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.032104</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.781560</td>\n",
       "      <td>0.008870</td>\n",
       "      <td>0.726299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 2, 'class_weight': 'balanced', 'penalty'...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.714325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.689256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714050</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.735354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026898</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.026970</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_score_time  mean_test_score  mean_train_score param_C  \\\n",
       "0        0.677820         0.009528         0.500000          0.500000    0.01   \n",
       "1        1.711092         0.008939         0.705840          0.985044    0.01   \n",
       "2        0.822581         0.008945         0.581648          0.612603    0.05   \n",
       "3        2.510007         0.009104         0.727245          0.999985    0.05   \n",
       "4        0.925438         0.008947         0.654279          0.728097     0.1   \n",
       "5        3.219833         0.009413         0.728558          1.000000     0.1   \n",
       "6        1.090583         0.008933         0.685464          0.996418     0.5   \n",
       "7        3.888121         0.008892         0.727750          1.000000     0.5   \n",
       "8        1.136910         0.008932         0.680606          1.000000       1   \n",
       "9        4.715721         0.008895         0.726951          1.000000       1   \n",
       "10       1.174981         0.008955         0.678292          1.000000       2   \n",
       "11       4.781560         0.008870         0.726299          1.000000       2   \n",
       "\n",
       "   param_class_weight param_penalty  \\\n",
       "0            balanced            l1   \n",
       "1            balanced            l2   \n",
       "2            balanced            l1   \n",
       "3            balanced            l2   \n",
       "4            balanced            l1   \n",
       "5            balanced            l2   \n",
       "6            balanced            l1   \n",
       "7            balanced            l2   \n",
       "8            balanced            l1   \n",
       "9            balanced            l2   \n",
       "10           balanced            l1   \n",
       "11           balanced            l2   \n",
       "\n",
       "                                               params  rank_test_score  \\\n",
       "0   {'C': 0.01, 'class_weight': 'balanced', 'penal...               12   \n",
       "1   {'C': 0.01, 'class_weight': 'balanced', 'penal...                6   \n",
       "2   {'C': 0.05, 'class_weight': 'balanced', 'penal...               11   \n",
       "3   {'C': 0.05, 'class_weight': 'balanced', 'penal...                3   \n",
       "4   {'C': 0.1, 'class_weight': 'balanced', 'penalt...               10   \n",
       "5   {'C': 0.1, 'class_weight': 'balanced', 'penalt...                1   \n",
       "6   {'C': 0.5, 'class_weight': 'balanced', 'penalt...                7   \n",
       "7   {'C': 0.5, 'class_weight': 'balanced', 'penalt...                2   \n",
       "8   {'C': 1, 'class_weight': 'balanced', 'penalty'...                8   \n",
       "9   {'C': 1, 'class_weight': 'balanced', 'penalty'...                4   \n",
       "10  {'C': 2, 'class_weight': 'balanced', 'penalty'...                9   \n",
       "11  {'C': 2, 'class_weight': 'balanced', 'penalty'...                5   \n",
       "\n",
       "    split0_test_score       ...         split7_test_score  split7_train_score  \\\n",
       "0            0.500000       ...                  0.500000            0.500000   \n",
       "1            0.709091       ...                  0.695500            0.985170   \n",
       "2            0.608815       ...                  0.603581            0.601626   \n",
       "3            0.721396       ...                  0.702112            1.000000   \n",
       "4            0.704132       ...                  0.706428            0.726941   \n",
       "5            0.721488       ...                  0.698714            1.000000   \n",
       "6            0.683747       ...                  0.639302            0.996742   \n",
       "7            0.717264       ...                  0.692929            1.000000   \n",
       "8            0.681543       ...                  0.633701            1.000000   \n",
       "9            0.714876       ...                  0.690542            1.000000   \n",
       "10           0.690083       ...                  0.637649            1.000000   \n",
       "11           0.714325       ...                  0.689256            1.000000   \n",
       "\n",
       "    split8_test_score  split8_train_score  split9_test_score  \\\n",
       "0            0.500000            0.500000           0.500000   \n",
       "1            0.686318            0.985660           0.721028   \n",
       "2            0.511203            0.617794           0.622498   \n",
       "3            0.709091            0.999998           0.732415   \n",
       "4            0.621028            0.729782           0.687879   \n",
       "5            0.710193            1.000000           0.733884   \n",
       "6            0.662902            0.996333           0.699816   \n",
       "7            0.713131            1.000000           0.733792   \n",
       "8            0.652525            1.000000           0.698990   \n",
       "9            0.713039            1.000000           0.735629   \n",
       "10           0.651056            1.000000           0.706428   \n",
       "11           0.714050            1.000000           0.735354   \n",
       "\n",
       "    split9_train_score  std_fit_time  std_score_time  std_test_score  \\\n",
       "0             0.500000      0.048030        0.001429        0.000000   \n",
       "1             0.985814      0.122934        0.000086        0.012510   \n",
       "2             0.596533      0.028789        0.000083        0.046098   \n",
       "3             0.999992      0.063607        0.000652        0.019795   \n",
       "4             0.725248      0.024421        0.000081        0.041721   \n",
       "5             1.000000      0.080889        0.000949        0.022264   \n",
       "6             0.996322      0.032679        0.000061        0.028133   \n",
       "7             1.000000      0.171257        0.000078        0.025996   \n",
       "8             1.000000      0.058563        0.000055        0.032449   \n",
       "9             1.000000      0.072102        0.000071        0.026764   \n",
       "10            1.000000      0.048649        0.000106        0.032104   \n",
       "11            1.000000      0.026898        0.000087        0.026970   \n",
       "\n",
       "    std_train_score  \n",
       "0      0.000000e+00  \n",
       "1      8.120014e-04  \n",
       "2      1.033102e-02  \n",
       "3      1.855346e-05  \n",
       "4      4.020678e-03  \n",
       "5      0.000000e+00  \n",
       "6      2.730590e-04  \n",
       "7      0.000000e+00  \n",
       "8      6.802027e-07  \n",
       "9      0.000000e+00  \n",
       "10     0.000000e+00  \n",
       "11     0.000000e+00  \n",
       "\n",
       "[12 rows x 33 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Pickle created----\n"
     ]
    }
   ],
   "source": [
    "# Creation of the pickle which is goig to store the model.\n",
    "\n",
    "pickle_out = open(\"LR_roc_auc_HOG_2.pickle\",\"wb\") #Modify with the name of the dataset you're saving\n",
    "pickle.dump(gs.cv_results_, pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "print(\"----Pickle created----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = gs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'C': 0.1, 'class_weight': 'balanced', 'penalty': 'l2'}\n",
      "Best roc_auc score: 0.728558\n"
     ]
    }
   ],
   "source": [
    "print(\"Best hyperparameters: {}\".format(gs.best_params_))\n",
    "print(\"Best roc_auc score: {:3f}\".format(gs.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=31, shuffle=True),\n",
      "       error_score='raise',\n",
      "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 2], 'penalty': ['l1', 'l2'], 'class_weight': ['balanced']},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "       scoring='roc_auc', verbose=3):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.61      0.59      0.60       480\n",
      "          1       0.56      0.59      0.57       432\n",
      "\n",
      "avg / total       0.59      0.59      0.59       912\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(gs, metrics.classification_report(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\\\\n",
    "\\\\\n",
    "\\\\\n",
    "\\\\\n",
    "\\\\\n",
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training----\n",
      "Fitting 4 folds for each of 8 candidates, totalling 32 fits\n",
      "[CV] C=1, class_weight=balanced, gamma=0.001, kernel=rbf .............\n",
      "[CV]  C=1, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.6675806451612903, total= 3.3min\n",
      "[CV] C=1, class_weight=balanced, gamma=0.001, kernel=rbf .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  5.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.65366568914956, total= 3.3min\n",
      "[CV] C=1, class_weight=balanced, gamma=0.001, kernel=rbf .............\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 11.6min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.6483327199116673, total= 3.4min\n",
      "[CV] C=1, class_weight=balanced, gamma=0.001, kernel=rbf .............\n",
      "[CV]  C=1, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.6789547294810453, total= 3.3min\n",
      "[CV] C=1, class_weight=balanced, gamma=0.0001, kernel=rbf ............\n",
      "[CV]  C=1, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.5567741935483872, total= 3.3min\n",
      "[CV] C=1, class_weight=balanced, gamma=0.0001, kernel=rbf ............\n",
      "[CV]  C=1, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.555733137829912, total= 3.3min\n",
      "[CV] C=1, class_weight=balanced, gamma=0.0001, kernel=rbf ............\n",
      "[CV]  C=1, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.5578505704821494, total= 3.3min\n",
      "[CV] C=1, class_weight=balanced, gamma=0.0001, kernel=rbf ............\n",
      "[CV]  C=1, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.5848656606551343, total= 3.3min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.001, kernel=rbf ............\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8312023460410557, total= 3.2min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.001, kernel=rbf ............\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8508064516129032, total= 3.2min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.001, kernel=rbf ............\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.861612072138388, total= 3.2min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.001, kernel=rbf ............\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8270445344129553, total= 3.3min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.0001, kernel=rbf ...........\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.6497360703812316, total= 3.1min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.0001, kernel=rbf ...........\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.6634310850439883, total= 3.1min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.0001, kernel=rbf ...........\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.6454324622745675, total= 3.1min\n",
      "[CV] C=10, class_weight=balanced, gamma=0.0001, kernel=rbf ...........\n",
      "[CV]  C=10, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.6716672800883328, total= 3.1min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.001, kernel=rbf ...........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8312023460410557, total= 3.2min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.001, kernel=rbf ...........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8514222873900295, total= 3.2min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.001, kernel=rbf ...........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8612587412587412, total= 3.2min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.001, kernel=rbf ...........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8276186970923812, total= 3.3min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.0001, kernel=rbf ..........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.719398826979472, total= 3.1min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.0001, kernel=rbf ..........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.7597360703812317, total= 3.1min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.0001, kernel=rbf ..........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.7579683474420318, total= 3.1min\n",
      "[CV] C=100, class_weight=balanced, gamma=0.0001, kernel=rbf ..........\n",
      "[CV]  C=100, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.7294074346705925, total= 3.1min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.001, kernel=rbf ..........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8312023460410557, total= 3.2min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.001, kernel=rbf ..........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8514222873900295, total= 3.2min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.001, kernel=rbf ..........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8612587412587412, total= 3.2min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.001, kernel=rbf ..........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.8276186970923812, total= 3.3min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf .........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.7187536656891496, total= 3.1min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf .........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.7621847507331377, total= 3.1min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf .........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.7585866764814133, total= 3.1min\n",
      "[CV] C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf .........\n",
      "[CV]  C=1000, class_weight=balanced, gamma=0.0001, kernel=rbf, score=0.7299668752300331, total= 3.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed: 179.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Ended----\n"
     ]
    }
   ],
   "source": [
    "# SVM\n",
    "\n",
    "print(\"----Training----\")\n",
    "\n",
    "k_fold = StratifiedKFold(n_splits=4, shuffle=True, random_state=31)\n",
    "\n",
    "model = models_and_hyperparameters['SVM'][0]\n",
    "hyperparameters = models_and_hyperparameters['SVM'][1]\n",
    "\n",
    "gs = GridSearchCV(model, \n",
    "                   param_grid = hyperparameters,\n",
    "                   cv=k_fold, \n",
    "                   scoring='roc_auc',\n",
    "                   verbose=3,\n",
    "                   return_train_score=True)\n",
    "\n",
    "gs.fit(X_train, y_train)\n",
    "\n",
    "print(\"----Training Ended----\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_class_weight</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>146.826307</td>\n",
       "      <td>51.460305</td>\n",
       "      <td>0.662132</td>\n",
       "      <td>0.945796</td>\n",
       "      <td>1</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'class_weight': 'balanced', 'gamma': ...</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.653666</td>\n",
       "      <td>0.940606</td>\n",
       "      <td>0.648333</td>\n",
       "      <td>0.941247</td>\n",
       "      <td>0.678955</td>\n",
       "      <td>0.954987</td>\n",
       "      <td>1.152600</td>\n",
       "      <td>1.288901</td>\n",
       "      <td>0.011984</td>\n",
       "      <td>5.753180e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>146.233172</td>\n",
       "      <td>51.089052</td>\n",
       "      <td>0.563799</td>\n",
       "      <td>0.754103</td>\n",
       "      <td>1</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'class_weight': 'balanced', 'gamma': ...</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555733</td>\n",
       "      <td>0.740624</td>\n",
       "      <td>0.557851</td>\n",
       "      <td>0.753990</td>\n",
       "      <td>0.584866</td>\n",
       "      <td>0.756214</td>\n",
       "      <td>0.155792</td>\n",
       "      <td>0.025150</td>\n",
       "      <td>0.012178</td>\n",
       "      <td>8.915632e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>145.785899</td>\n",
       "      <td>49.023252</td>\n",
       "      <td>0.842665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10, 'class_weight': 'balanced', 'gamma':...</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.850806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.861612</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.827045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095257</td>\n",
       "      <td>0.134472</td>\n",
       "      <td>0.014145</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>138.622670</td>\n",
       "      <td>47.962976</td>\n",
       "      <td>0.657566</td>\n",
       "      <td>0.927154</td>\n",
       "      <td>10</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10, 'class_weight': 'balanced', 'gamma':...</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.663431</td>\n",
       "      <td>0.921055</td>\n",
       "      <td>0.645432</td>\n",
       "      <td>0.924170</td>\n",
       "      <td>0.671667</td>\n",
       "      <td>0.935883</td>\n",
       "      <td>0.718764</td>\n",
       "      <td>0.360785</td>\n",
       "      <td>0.010506</td>\n",
       "      <td>5.532233e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>145.801156</td>\n",
       "      <td>49.043213</td>\n",
       "      <td>0.842874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 100, 'class_weight': 'balanced', 'gamma'...</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.851422</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.861259</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.827619</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106188</td>\n",
       "      <td>0.155340</td>\n",
       "      <td>0.013961</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>142.107442</td>\n",
       "      <td>43.296240</td>\n",
       "      <td>0.741626</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>100</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 100, 'class_weight': 'balanced', 'gamma'...</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.759736</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.757968</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.729407</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182213</td>\n",
       "      <td>0.328031</td>\n",
       "      <td>0.017598</td>\n",
       "      <td>8.167933e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>145.783166</td>\n",
       "      <td>49.049437</td>\n",
       "      <td>0.842874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1000, 'class_weight': 'balanced', 'gamma...</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.851422</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.861259</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.827619</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091702</td>\n",
       "      <td>0.148445</td>\n",
       "      <td>0.013961</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>143.194240</td>\n",
       "      <td>43.286253</td>\n",
       "      <td>0.742371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>balanced</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1000, 'class_weight': 'balanced', 'gamma...</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.762185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.729967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.689200</td>\n",
       "      <td>0.306349</td>\n",
       "      <td>0.018491</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score param_C  \\\n",
       "0     146.826307        51.460305         0.662132          0.945796       1   \n",
       "1     146.233172        51.089052         0.563799          0.754103       1   \n",
       "2     145.785899        49.023252         0.842665          1.000000      10   \n",
       "3     138.622670        47.962976         0.657566          0.927154      10   \n",
       "4     145.801156        49.043213         0.842874          1.000000     100   \n",
       "5     142.107442        43.296240         0.741626          0.999999     100   \n",
       "6     145.783166        49.049437         0.842874          1.000000    1000   \n",
       "7     143.194240        43.286253         0.742371          1.000000    1000   \n",
       "\n",
       "  param_class_weight param_gamma param_kernel  \\\n",
       "0           balanced       0.001          rbf   \n",
       "1           balanced      0.0001          rbf   \n",
       "2           balanced       0.001          rbf   \n",
       "3           balanced      0.0001          rbf   \n",
       "4           balanced       0.001          rbf   \n",
       "5           balanced      0.0001          rbf   \n",
       "6           balanced       0.001          rbf   \n",
       "7           balanced      0.0001          rbf   \n",
       "\n",
       "                                              params  rank_test_score  \\\n",
       "0  {'C': 1, 'class_weight': 'balanced', 'gamma': ...                6   \n",
       "1  {'C': 1, 'class_weight': 'balanced', 'gamma': ...                8   \n",
       "2  {'C': 10, 'class_weight': 'balanced', 'gamma':...                3   \n",
       "3  {'C': 10, 'class_weight': 'balanced', 'gamma':...                7   \n",
       "4  {'C': 100, 'class_weight': 'balanced', 'gamma'...                1   \n",
       "5  {'C': 100, 'class_weight': 'balanced', 'gamma'...                5   \n",
       "6  {'C': 1000, 'class_weight': 'balanced', 'gamma...                1   \n",
       "7  {'C': 1000, 'class_weight': 'balanced', 'gamma...                4   \n",
       "\n",
       "        ...         split1_test_score  split1_train_score  split2_test_score  \\\n",
       "0       ...                  0.653666            0.940606           0.648333   \n",
       "1       ...                  0.555733            0.740624           0.557851   \n",
       "2       ...                  0.850806            1.000000           0.861612   \n",
       "3       ...                  0.663431            0.921055           0.645432   \n",
       "4       ...                  0.851422            1.000000           0.861259   \n",
       "5       ...                  0.759736            0.999998           0.757968   \n",
       "6       ...                  0.851422            1.000000           0.861259   \n",
       "7       ...                  0.762185            1.000000           0.758587   \n",
       "\n",
       "   split2_train_score  split3_test_score  split3_train_score  std_fit_time  \\\n",
       "0            0.941247           0.678955            0.954987      1.152600   \n",
       "1            0.753990           0.584866            0.756214      0.155792   \n",
       "2            1.000000           0.827045            1.000000      0.095257   \n",
       "3            0.924170           0.671667            0.935883      0.718764   \n",
       "4            1.000000           0.827619            1.000000      0.106188   \n",
       "5            1.000000           0.729407            1.000000      0.182213   \n",
       "6            1.000000           0.827619            1.000000      0.091702   \n",
       "7            1.000000           0.729967            1.000000      0.689200   \n",
       "\n",
       "   std_score_time  std_test_score  std_train_score  \n",
       "0        1.288901        0.011984     5.753180e-03  \n",
       "1        0.025150        0.012178     8.915632e-03  \n",
       "2        0.134472        0.014145     0.000000e+00  \n",
       "3        0.360785        0.010506     5.532233e-03  \n",
       "4        0.155340        0.013961     0.000000e+00  \n",
       "5        0.328031        0.017598     8.167933e-07  \n",
       "6        0.148445        0.013961     0.000000e+00  \n",
       "7        0.306349        0.018491     0.000000e+00  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Pickle created----\n"
     ]
    }
   ],
   "source": [
    "# Creation of the pickle which is goig to store the model.\n",
    "\n",
    "pickle_out = open(\"SVM_roc_auc_HOG_2.pickle\",\"wb\") #Modify with the name of the dataset you're saving\n",
    "pickle.dump(gs.cv_results_, pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "print(\"----Pickle created----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = gs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'C': 100, 'class_weight': 'balanced', 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "Best roc_auc score: 0.842874\n"
     ]
    }
   ],
   "source": [
    "print(\"Best hyperparameters: {}\".format(gs.best_params_))\n",
    "print(\"Best roc_auc score: {:3f}\".format(gs.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "GridSearchCV(cv=StratifiedKFold(n_splits=4, random_state=31, shuffle=True),\n",
      "       error_score='raise',\n",
      "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf'], 'class_weight': ['balanced']},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "       scoring='roc_auc', verbose=3):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.61      0.65      0.63       480\n",
      "          1       0.58      0.55      0.56       432\n",
      "\n",
      "avg / total       0.60      0.60      0.60       912\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(gs, metrics.classification_report(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "//\n",
    "//\n",
    "//\n",
    "//\n",
    "//\n",
    "\n",
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training----\n",
      "Fitting 4 folds for each of 54 candidates, totalling 216 fits\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.7981818181818182, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.761510263929619, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    8.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.7761280824438719, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.7554876702245125, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.8247140762463343, total=   7.5s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.7964662756598241, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.7904600662495399, total=   7.5s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.7842841369157159, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.8295674486803519, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.8191348973607038, total=  14.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.8084136915715863, total=  14.7s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.7822524843577475, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.7769574780058651, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.7763269794721408, total=   3.7s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.776474052263526, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.7794258373205742, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.8155205278592376, total=   7.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.7813489736070381, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.8076996687523004, total=   7.5s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.7911152005888848, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8151026392961878, total=  14.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8087976539589442, total=  14.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8139565697460434, total=  14.5s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8077732793522265, total=  14.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.770117302052786, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.7538123167155424, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.7963930806036069, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.7434670592565329, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.8025953079178887, total=   7.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.7848826979472142, total=   7.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.7686124401913876, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.7628119249171881, total=   7.1s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.8125073313782992, total=  14.1s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.823533724340176, total=  14.1s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.8112476996687523, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.8083327199116672, total=  14.1s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.7703885630498534, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.7808284457478007, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.7623113728376887, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.7559440559440559, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.8129765395894428, total=   7.5s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.7965395894428153, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.7856827383143172, total=   7.6s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.7977769598822231, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.8302639296187684, total=  14.6s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.8124486803519062, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.7951048951048951, total=  14.6s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.7898932646301067, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.7775146627565983, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.7672653958944282, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.7719985277880015, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.7670960618329039, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.8153299120234603, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.7961950146627568, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.7942142068457859, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.7902465955097535, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.8204985337243402, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.8107184750733138, total=  14.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.8129554655870445, total=  14.6s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.7970555760029443, total=  14.1s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.8010850439882699, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.754457478005865, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.778122929701877, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.74702981229297, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.8032697947214077, total=   7.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.8037390029325514, total=   7.1s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.7617077659182921, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.7769304379830695, total=   7.1s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.8339882697947214, total=  13.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.8002346041055719, total=  13.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.8031652557968347, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.8034155318365844, total=  13.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.77933284457478, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.7311656891495601, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.76370997423629, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.7632683106367317, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.8067082111436951, total=   7.5s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.791524926686217, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.7888921604711079, total=   7.7s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.8132351858667648, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.8383064516129032, total=  14.5s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.811400293255132, total=  14.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.804181082075819, total=  14.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.8062495399337505, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.7812903225806451, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.7559457478005867, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.7812145748987854, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.7711888111888112, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.799149560117302, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.7837609970674486, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.8037394184762606, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.7799631947000368, total=   7.4s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.8351759530791788, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.8064516129032258, total=  14.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.8145307324254693, total=  14.6s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.7934044902465955, total=  14.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.7851759530791789, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.7406598240469209, total=   4.0s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.7518145012881855, total=   3.9s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.7411115200588884, total=   3.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.8132404692082111, total=   7.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.781950146627566, total=   7.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.7736768494663231, total=   7.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.7714390872285608, total=   7.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.8242521994134897, total=  14.2s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.7923460410557185, total=  13.8s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.813352962826647, total=  14.3s\n",
      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.802811924917188, total=  13.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.8135850439882698, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.8239222873900294, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.7856017666543983, total=   6.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50, score=0.8178947368421052, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.8492155425219942, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.8261290322580646, total=  11.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.8380272359219728, total=  11.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100, score=0.818468899521531, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.8503152492668622, total=  22.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.855234604105572, total=  23.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.8566507177033492, total=  23.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200, score=0.833721015826279, total=  22.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.8297873900293256, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.8323460410557185, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.820080971659919, total=   6.2s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50, score=0.7952962826647039, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.837492668621701, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.8389516129032257, total=  11.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.8370776591829224, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100, score=0.8177033492822967, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8658651026392962, total=  22.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8505865102639296, total=  22.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8497165991902835, total=  22.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200, score=0.8260875966139125, total=  22.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.827375366568915, total=   6.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.8235190615835777, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.8254692675745309, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.7822156790577843, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.8326539589442815, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.8357624633431084, total=  11.2s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.8314758925285242, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100, score=0.8084357747515643, total=  11.3s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.8603225806451613, total=  22.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.8461730205278593, total=  21.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.8354508649245491, total=  23.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.8207287449392712, total=  22.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.8052272727272727, total=   6.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.7996847507331378, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.829216047110784, total=   6.2s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50, score=0.811232977548767, total=   6.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.8451612903225806, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.8420967741935484, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.8321972764078028, total=  11.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100, score=0.8061244019138756, total=  11.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.8648900293255132, total=  22.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.8482038123167156, total=  22.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.8487817445712184, total=  23.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200, score=0.8202576370997423, total=  22.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.8227052785923753, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.8130131964809385, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.8009642988590358, total=   6.2s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50, score=0.7973941847626058, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.8422287390029325, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.8457404692082111, total=  11.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.8449171880750828, total=  11.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.8166801619433199, total=  11.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.8636217008797654, total=  22.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.8544721407624634, total=  22.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.8314022819285978, total=  23.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.8212881854987117, total=  22.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.835967741935484, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.7981671554252199, total=   5.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.8154140596245859, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.785380934854619, total=   5.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.828900293255132, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.8369941348973607, total=  11.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.7928597718071402, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=100, score=0.8163709974236291, total=  11.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.847067448680352, total=  22.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.8511436950146628, total=  22.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.8313433934486566, total=  22.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.8217887375782111, total=  21.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.82316715542522, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.819450146627566, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.8153404490246595, total=   6.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.7799116672800883, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.8297653958944282, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.8440615835777127, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.8299963194700035, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=100, score=0.8148325358851676, total=  11.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.8468841642228738, total=  23.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.8323900293255132, total=  22.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.845079131394921, total=  23.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=200, score=0.8355833640044167, total=  22.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.8222580645161292, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.8106964809384165, total=   5.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.8076334192123666, total=   6.3s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=50, score=0.7876113360323886, total=   6.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.8473607038123168, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.8386217008797655, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.8346853146853147, total=  11.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=100, score=0.8256238498343761, total=  11.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.8611583577712609, total=  22.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.8493108504398826, total=  22.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.8356128082443872, total=  23.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=5, n_estimators=200, score=0.83682002208318, total=  22.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.831524926686217, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.8081524926686217, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.8142951785057048, total=   6.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=50, score=0.7597791682002207, total=   5.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.8547360703812316, total=  11.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.8367008797653959, total=  11.2s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.8150901729849099, total=  11.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=100, score=0.8168421052631578, total=  11.3s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.859208211143695, total=  22.5s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.8437976539589442, total=  22.1s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.8449024659550975, total=  22.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200 \n",
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=10, n_estimators=200, score=0.8239087228560913, total=  22.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 216 out of 216 | elapsed: 40.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Ended----\n"
     ]
    }
   ],
   "source": [
    "#RANDOM FOREST\n",
    "\n",
    "print(\"----Training----\")\n",
    "\n",
    "k_fold = StratifiedKFold(n_splits=4, shuffle=True, random_state=31)\n",
    "\n",
    "model = models_and_hyperparameters['RFC'][0]\n",
    "hyperparameters = models_and_hyperparameters['RFC'][1]\n",
    "\n",
    "gs = GridSearchCV(model, \n",
    "                   param_grid = hyperparameters,\n",
    "                   cv=k_fold, \n",
    "                   scoring='roc_auc',\n",
    "                   verbose=3,\n",
    "                   return_train_score=True)\n",
    "\n",
    "gs.fit(X_train, y_train)\n",
    "\n",
    "print(\"----Training Ended----\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_bootstrap</th>\n",
       "      <th>param_class_weight</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>...</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.883076</td>\n",
       "      <td>0.056426</td>\n",
       "      <td>0.772834</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.761510</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.755488</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.059498</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.016454</td>\n",
       "      <td>7.850462e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.364434</td>\n",
       "      <td>0.065167</td>\n",
       "      <td>0.798992</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.796466</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.790460</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.784284</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.081002</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.015472</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.343236</td>\n",
       "      <td>0.083509</td>\n",
       "      <td>0.809856</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.819135</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.808414</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.782252</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.169241</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.017595</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.841459</td>\n",
       "      <td>0.056535</td>\n",
       "      <td>0.777295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.776327</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776474</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.779426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111655</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.262926</td>\n",
       "      <td>0.065237</td>\n",
       "      <td>0.798921</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.781349</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.807700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791115</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100838</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.013442</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.242902</td>\n",
       "      <td>0.083361</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.813957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.807773</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.143779</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.003169</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.789865</td>\n",
       "      <td>0.056491</td>\n",
       "      <td>0.765944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.753812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.796393</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.743467</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.066805</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.019973</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.157019</td>\n",
       "      <td>0.064827</td>\n",
       "      <td>0.779739</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.784883</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.768612</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.762812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.103096</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.015487</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.061513</td>\n",
       "      <td>0.082914</td>\n",
       "      <td>0.813909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823534</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.811248</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.808333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.085921</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.005763</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.865893</td>\n",
       "      <td>0.056390</td>\n",
       "      <td>0.767376</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780828</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.762311</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.755944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.071858</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.009306</td>\n",
       "      <td>7.850462e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.363726</td>\n",
       "      <td>0.065179</td>\n",
       "      <td>0.798250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.796540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.785683</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.797777</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.091798</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.009721</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.351210</td>\n",
       "      <td>0.083515</td>\n",
       "      <td>0.806941</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.812449</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.795105</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.789893</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.138050</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.855806</td>\n",
       "      <td>0.056559</td>\n",
       "      <td>0.770970</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767265</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.771999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.767096</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037405</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.302448</td>\n",
       "      <td>0.065144</td>\n",
       "      <td>0.799003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.796195</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.794214</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.790247</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.044639</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.009672</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.227999</td>\n",
       "      <td>0.083223</td>\n",
       "      <td>0.810312</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.810718</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.812955</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.797056</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.178168</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.008464</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.775522</td>\n",
       "      <td>0.056340</td>\n",
       "      <td>0.770181</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.754457</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.778123</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.747030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.028326</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.021225</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.143026</td>\n",
       "      <td>0.064954</td>\n",
       "      <td>0.786428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.803739</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.761708</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090220</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.017920</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.896407</td>\n",
       "      <td>0.083036</td>\n",
       "      <td>0.810208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800235</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.803165</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.803416</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.203188</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.013795</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.906514</td>\n",
       "      <td>0.057060</td>\n",
       "      <td>0.759365</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731166</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.763710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.763268</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037976</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.017530</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.427309</td>\n",
       "      <td>0.065750</td>\n",
       "      <td>0.800089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.791525</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788892</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.813235</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.139019</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.010188</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.279343</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.815044</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.804181</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.806250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.122286</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.013694</td>\n",
       "      <td>7.850462e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.794318</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.772406</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.755946</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.781215</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.771189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.061120</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.010359</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.316648</td>\n",
       "      <td>0.065545</td>\n",
       "      <td>0.791653</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783761</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.803739</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.779963</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.038227</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14.234107</td>\n",
       "      <td>0.083495</td>\n",
       "      <td>0.812399</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.814531</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.793404</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.145256</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.015163</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.794309</td>\n",
       "      <td>0.056801</td>\n",
       "      <td>0.754698</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.740660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.751815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.741112</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.066710</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.018164</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.129988</td>\n",
       "      <td>0.065250</td>\n",
       "      <td>0.785089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.781950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.773677</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.771439</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.051585</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.016729</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.952189</td>\n",
       "      <td>0.083118</td>\n",
       "      <td>0.808191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': 'balanced'...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.792346</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.813353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.802812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.198852</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.011885</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.948568</td>\n",
       "      <td>0.057591</td>\n",
       "      <td>0.810259</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823922</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.785602</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.817895</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.077553</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.014693</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11.602479</td>\n",
       "      <td>0.066677</td>\n",
       "      <td>0.832965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.826129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.838027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.818469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.172401</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.011690</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>22.856937</td>\n",
       "      <td>0.086145</td>\n",
       "      <td>0.848984</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.855235</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.856651</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833721</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.347970</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.009115</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5.970701</td>\n",
       "      <td>0.057635</td>\n",
       "      <td>0.819389</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.832346</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.820081</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.795296</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.088893</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.014634</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11.490280</td>\n",
       "      <td>0.066691</td>\n",
       "      <td>0.832812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.838952</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.837078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.817703</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.127202</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.008745</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>22.539863</td>\n",
       "      <td>0.085868</td>\n",
       "      <td>0.848074</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.850587</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.849717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.826088</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.147879</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.014220</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.898930</td>\n",
       "      <td>0.057406</td>\n",
       "      <td>0.814655</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.825469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.782216</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.103827</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.018767</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>11.357329</td>\n",
       "      <td>0.066361</td>\n",
       "      <td>0.827089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.835762</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.831476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.808436</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.177012</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.010876</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>22.315144</td>\n",
       "      <td>0.085467</td>\n",
       "      <td>0.840681</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846173</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.835451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.820729</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.469449</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.014503</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6.012006</td>\n",
       "      <td>0.057539</td>\n",
       "      <td>0.811332</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.799685</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.829216</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.811233</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.127330</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.011098</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>11.527011</td>\n",
       "      <td>0.066598</td>\n",
       "      <td>0.831407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.842097</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.832197</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.806124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.104327</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.015354</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>22.854173</td>\n",
       "      <td>0.086401</td>\n",
       "      <td>0.845544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.848204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.848782</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.820258</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.390921</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.016054</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.955191</td>\n",
       "      <td>0.057573</td>\n",
       "      <td>0.808528</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.813013</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.800964</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.797394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.080674</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.010029</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>11.514107</td>\n",
       "      <td>0.066696</td>\n",
       "      <td>0.837398</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.845740</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.844917</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.816680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.148101</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>22.604719</td>\n",
       "      <td>0.086022</td>\n",
       "      <td>0.842712</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.854472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.831402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.821288</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.274334</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.017047</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5.852076</td>\n",
       "      <td>0.057427</td>\n",
       "      <td>0.808740</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.798167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815414</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.785381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.081368</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.018997</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>11.369636</td>\n",
       "      <td>0.066380</td>\n",
       "      <td>0.818795</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.792860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.816371</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.217585</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.016670</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22.236570</td>\n",
       "      <td>0.084747</td>\n",
       "      <td>0.837847</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.851144</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.831343</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.821789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.456123</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.011853</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5.931142</td>\n",
       "      <td>0.056714</td>\n",
       "      <td>0.809479</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.819450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815340</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.779912</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.073195</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.017283</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>11.529241</td>\n",
       "      <td>0.065904</td>\n",
       "      <td>0.829671</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.844062</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.829996</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.814833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.121699</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.010336</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>22.875527</td>\n",
       "      <td>0.085420</td>\n",
       "      <td>0.839984</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.832390</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.845079</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.835583</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.378941</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.006137</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.963339</td>\n",
       "      <td>0.056952</td>\n",
       "      <td>0.807059</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.810696</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.807633</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.787611</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.157689</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>7.850462e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>11.458664</td>\n",
       "      <td>0.066073</td>\n",
       "      <td>0.836579</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.838622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.834685</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.825624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.094723</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.007811</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>22.662922</td>\n",
       "      <td>0.085376</td>\n",
       "      <td>0.845735</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.849311</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.835613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.836820</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.395849</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5.879248</td>\n",
       "      <td>0.056877</td>\n",
       "      <td>0.803454</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808152</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.814295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.759779</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.115147</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.026617</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>11.341609</td>\n",
       "      <td>0.065686</td>\n",
       "      <td>0.830857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.816842</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.190041</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.016198</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>22.309566</td>\n",
       "      <td>0.084947</td>\n",
       "      <td>0.842962</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>balanced</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>{'bootstrap': False, 'class_weight': 'balanced...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.843798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.844902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.823909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.252769</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.012563</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "0        3.883076         0.056426         0.772834               1.0   \n",
       "1        7.364434         0.065167         0.798992               1.0   \n",
       "2       14.343236         0.083509         0.809856               1.0   \n",
       "3        3.841459         0.056535         0.777295               1.0   \n",
       "4        7.262926         0.065237         0.798921               1.0   \n",
       "5       14.242902         0.083361         0.811408               1.0   \n",
       "6        3.789865         0.056491         0.765944               1.0   \n",
       "7        7.157019         0.064827         0.779739               1.0   \n",
       "8       14.061513         0.082914         0.813909               1.0   \n",
       "9        3.865893         0.056390         0.767376               1.0   \n",
       "10       7.363726         0.065179         0.798250               1.0   \n",
       "11      14.351210         0.083515         0.806941               1.0   \n",
       "12       3.855806         0.056559         0.770970               1.0   \n",
       "13       7.302448         0.065144         0.799003               1.0   \n",
       "14      14.227999         0.083223         0.810312               1.0   \n",
       "15       3.775522         0.056340         0.770181               1.0   \n",
       "16       7.143026         0.064954         0.786428               1.0   \n",
       "17      13.896407         0.083036         0.810208               1.0   \n",
       "18       3.906514         0.057060         0.759365               1.0   \n",
       "19       7.427309         0.065750         0.800089               1.0   \n",
       "20      14.279343         0.083853         0.815044               1.0   \n",
       "21       3.794318         0.057000         0.772406               1.0   \n",
       "22       7.316648         0.065545         0.791653               1.0   \n",
       "23      14.234107         0.083495         0.812399               1.0   \n",
       "24       3.794309         0.056801         0.754698               1.0   \n",
       "25       7.129988         0.065250         0.785089               1.0   \n",
       "26      13.952189         0.083118         0.808191               1.0   \n",
       "27       5.948568         0.057591         0.810259               1.0   \n",
       "28      11.602479         0.066677         0.832965               1.0   \n",
       "29      22.856937         0.086145         0.848984               1.0   \n",
       "30       5.970701         0.057635         0.819389               1.0   \n",
       "31      11.490280         0.066691         0.832812               1.0   \n",
       "32      22.539863         0.085868         0.848074               1.0   \n",
       "33       5.898930         0.057406         0.814655               1.0   \n",
       "34      11.357329         0.066361         0.827089               1.0   \n",
       "35      22.315144         0.085467         0.840681               1.0   \n",
       "36       6.012006         0.057539         0.811332               1.0   \n",
       "37      11.527011         0.066598         0.831407               1.0   \n",
       "38      22.854173         0.086401         0.845544               1.0   \n",
       "39       5.955191         0.057573         0.808528               1.0   \n",
       "40      11.514107         0.066696         0.837398               1.0   \n",
       "41      22.604719         0.086022         0.842712               1.0   \n",
       "42       5.852076         0.057427         0.808740               1.0   \n",
       "43      11.369636         0.066380         0.818795               1.0   \n",
       "44      22.236570         0.084747         0.837847               1.0   \n",
       "45       5.931142         0.056714         0.809479               1.0   \n",
       "46      11.529241         0.065904         0.829671               1.0   \n",
       "47      22.875527         0.085420         0.839984               1.0   \n",
       "48       5.963339         0.056952         0.807059               1.0   \n",
       "49      11.458664         0.066073         0.836579               1.0   \n",
       "50      22.662922         0.085376         0.845735               1.0   \n",
       "51       5.879248         0.056877         0.803454               1.0   \n",
       "52      11.341609         0.065686         0.830857               1.0   \n",
       "53      22.309566         0.084947         0.842962               1.0   \n",
       "\n",
       "   param_bootstrap param_class_weight param_max_depth param_min_samples_split  \\\n",
       "0             True           balanced              50                       2   \n",
       "1             True           balanced              50                       2   \n",
       "2             True           balanced              50                       2   \n",
       "3             True           balanced              50                       5   \n",
       "4             True           balanced              50                       5   \n",
       "5             True           balanced              50                       5   \n",
       "6             True           balanced              50                      10   \n",
       "7             True           balanced              50                      10   \n",
       "8             True           balanced              50                      10   \n",
       "9             True           balanced              60                       2   \n",
       "10            True           balanced              60                       2   \n",
       "11            True           balanced              60                       2   \n",
       "12            True           balanced              60                       5   \n",
       "13            True           balanced              60                       5   \n",
       "14            True           balanced              60                       5   \n",
       "15            True           balanced              60                      10   \n",
       "16            True           balanced              60                      10   \n",
       "17            True           balanced              60                      10   \n",
       "18            True           balanced              70                       2   \n",
       "19            True           balanced              70                       2   \n",
       "20            True           balanced              70                       2   \n",
       "21            True           balanced              70                       5   \n",
       "22            True           balanced              70                       5   \n",
       "23            True           balanced              70                       5   \n",
       "24            True           balanced              70                      10   \n",
       "25            True           balanced              70                      10   \n",
       "26            True           balanced              70                      10   \n",
       "27           False           balanced              50                       2   \n",
       "28           False           balanced              50                       2   \n",
       "29           False           balanced              50                       2   \n",
       "30           False           balanced              50                       5   \n",
       "31           False           balanced              50                       5   \n",
       "32           False           balanced              50                       5   \n",
       "33           False           balanced              50                      10   \n",
       "34           False           balanced              50                      10   \n",
       "35           False           balanced              50                      10   \n",
       "36           False           balanced              60                       2   \n",
       "37           False           balanced              60                       2   \n",
       "38           False           balanced              60                       2   \n",
       "39           False           balanced              60                       5   \n",
       "40           False           balanced              60                       5   \n",
       "41           False           balanced              60                       5   \n",
       "42           False           balanced              60                      10   \n",
       "43           False           balanced              60                      10   \n",
       "44           False           balanced              60                      10   \n",
       "45           False           balanced              70                       2   \n",
       "46           False           balanced              70                       2   \n",
       "47           False           balanced              70                       2   \n",
       "48           False           balanced              70                       5   \n",
       "49           False           balanced              70                       5   \n",
       "50           False           balanced              70                       5   \n",
       "51           False           balanced              70                      10   \n",
       "52           False           balanced              70                      10   \n",
       "53           False           balanced              70                      10   \n",
       "\n",
       "   param_n_estimators                                             params  \\\n",
       "0                  50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "1                 100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "2                 200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "3                  50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "4                 100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "5                 200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "6                  50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "7                 100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "8                 200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "9                  50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "10                100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "11                200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "12                 50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "13                100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "14                200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "15                 50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "16                100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "17                200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "18                 50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "19                100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "20                200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "21                 50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "22                100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "23                200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "24                 50  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "25                100  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "26                200  {'bootstrap': True, 'class_weight': 'balanced'...   \n",
       "27                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "28                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "29                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "30                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "31                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "32                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "33                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "34                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "35                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "36                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "37                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "38                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "39                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "40                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "41                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "42                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "43                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "44                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "45                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "46                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "47                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "48                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "49                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "50                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "51                 50  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "52                100  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "53                200  {'bootstrap': False, 'class_weight': 'balanced...   \n",
       "\n",
       "         ...         split1_test_score  split1_train_score  split2_test_score  \\\n",
       "0        ...                  0.761510                 1.0           0.776128   \n",
       "1        ...                  0.796466                 1.0           0.790460   \n",
       "2        ...                  0.819135                 1.0           0.808414   \n",
       "3        ...                  0.776327                 1.0           0.776474   \n",
       "4        ...                  0.781349                 1.0           0.807700   \n",
       "5        ...                  0.808798                 1.0           0.813957   \n",
       "6        ...                  0.753812                 1.0           0.796393   \n",
       "7        ...                  0.784883                 1.0           0.768612   \n",
       "8        ...                  0.823534                 1.0           0.811248   \n",
       "9        ...                  0.780828                 1.0           0.762311   \n",
       "10       ...                  0.796540                 1.0           0.785683   \n",
       "11       ...                  0.812449                 1.0           0.795105   \n",
       "12       ...                  0.767265                 1.0           0.771999   \n",
       "13       ...                  0.796195                 1.0           0.794214   \n",
       "14       ...                  0.810718                 1.0           0.812955   \n",
       "15       ...                  0.754457                 1.0           0.778123   \n",
       "16       ...                  0.803739                 1.0           0.761708   \n",
       "17       ...                  0.800235                 1.0           0.803165   \n",
       "18       ...                  0.731166                 1.0           0.763710   \n",
       "19       ...                  0.791525                 1.0           0.788892   \n",
       "20       ...                  0.811400                 1.0           0.804181   \n",
       "21       ...                  0.755946                 1.0           0.781215   \n",
       "22       ...                  0.783761                 1.0           0.803739   \n",
       "23       ...                  0.806452                 1.0           0.814531   \n",
       "24       ...                  0.740660                 1.0           0.751815   \n",
       "25       ...                  0.781950                 1.0           0.773677   \n",
       "26       ...                  0.792346                 1.0           0.813353   \n",
       "27       ...                  0.823922                 1.0           0.785602   \n",
       "28       ...                  0.826129                 1.0           0.838027   \n",
       "29       ...                  0.855235                 1.0           0.856651   \n",
       "30       ...                  0.832346                 1.0           0.820081   \n",
       "31       ...                  0.838952                 1.0           0.837078   \n",
       "32       ...                  0.850587                 1.0           0.849717   \n",
       "33       ...                  0.823519                 1.0           0.825469   \n",
       "34       ...                  0.835762                 1.0           0.831476   \n",
       "35       ...                  0.846173                 1.0           0.835451   \n",
       "36       ...                  0.799685                 1.0           0.829216   \n",
       "37       ...                  0.842097                 1.0           0.832197   \n",
       "38       ...                  0.848204                 1.0           0.848782   \n",
       "39       ...                  0.813013                 1.0           0.800964   \n",
       "40       ...                  0.845740                 1.0           0.844917   \n",
       "41       ...                  0.854472                 1.0           0.831402   \n",
       "42       ...                  0.798167                 1.0           0.815414   \n",
       "43       ...                  0.836994                 1.0           0.792860   \n",
       "44       ...                  0.851144                 1.0           0.831343   \n",
       "45       ...                  0.819450                 1.0           0.815340   \n",
       "46       ...                  0.844062                 1.0           0.829996   \n",
       "47       ...                  0.832390                 1.0           0.845079   \n",
       "48       ...                  0.810696                 1.0           0.807633   \n",
       "49       ...                  0.838622                 1.0           0.834685   \n",
       "50       ...                  0.849311                 1.0           0.835613   \n",
       "51       ...                  0.808152                 1.0           0.814295   \n",
       "52       ...                  0.836701                 1.0           0.815090   \n",
       "53       ...                  0.843798                 1.0           0.844902   \n",
       "\n",
       "    split2_train_score  split3_test_score  split3_train_score  std_fit_time  \\\n",
       "0                  1.0           0.755488                 1.0      0.059498   \n",
       "1                  1.0           0.784284                 1.0      0.081002   \n",
       "2                  1.0           0.782252                 1.0      0.169241   \n",
       "3                  1.0           0.779426                 1.0      0.111655   \n",
       "4                  1.0           0.791115                 1.0      0.100838   \n",
       "5                  1.0           0.807773                 1.0      0.143779   \n",
       "6                  1.0           0.743467                 1.0      0.066805   \n",
       "7                  1.0           0.762812                 1.0      0.103096   \n",
       "8                  1.0           0.808333                 1.0      0.085921   \n",
       "9                  1.0           0.755944                 1.0      0.071858   \n",
       "10                 1.0           0.797777                 1.0      0.091798   \n",
       "11                 1.0           0.789893                 1.0      0.138050   \n",
       "12                 1.0           0.767096                 1.0      0.037405   \n",
       "13                 1.0           0.790247                 1.0      0.044639   \n",
       "14                 1.0           0.797056                 1.0      0.178168   \n",
       "15                 1.0           0.747030                 1.0      0.028326   \n",
       "16                 1.0           0.776930                 1.0      0.090220   \n",
       "17                 1.0           0.803416                 1.0      0.203188   \n",
       "18                 1.0           0.763268                 1.0      0.037976   \n",
       "19                 1.0           0.813235                 1.0      0.139019   \n",
       "20                 1.0           0.806250                 1.0      0.122286   \n",
       "21                 1.0           0.771189                 1.0      0.061120   \n",
       "22                 1.0           0.779963                 1.0      0.038227   \n",
       "23                 1.0           0.793404                 1.0      0.145256   \n",
       "24                 1.0           0.741112                 1.0      0.066710   \n",
       "25                 1.0           0.771439                 1.0      0.051585   \n",
       "26                 1.0           0.802812                 1.0      0.198852   \n",
       "27                 1.0           0.817895                 1.0      0.077553   \n",
       "28                 1.0           0.818469                 1.0      0.172401   \n",
       "29                 1.0           0.833721                 1.0      0.347970   \n",
       "30                 1.0           0.795296                 1.0      0.088893   \n",
       "31                 1.0           0.817703                 1.0      0.127202   \n",
       "32                 1.0           0.826088                 1.0      0.147879   \n",
       "33                 1.0           0.782216                 1.0      0.103827   \n",
       "34                 1.0           0.808436                 1.0      0.177012   \n",
       "35                 1.0           0.820729                 1.0      0.469449   \n",
       "36                 1.0           0.811233                 1.0      0.127330   \n",
       "37                 1.0           0.806124                 1.0      0.104327   \n",
       "38                 1.0           0.820258                 1.0      0.390921   \n",
       "39                 1.0           0.797394                 1.0      0.080674   \n",
       "40                 1.0           0.816680                 1.0      0.148101   \n",
       "41                 1.0           0.821288                 1.0      0.274334   \n",
       "42                 1.0           0.785381                 1.0      0.081368   \n",
       "43                 1.0           0.816371                 1.0      0.217585   \n",
       "44                 1.0           0.821789                 1.0      0.456123   \n",
       "45                 1.0           0.779912                 1.0      0.073195   \n",
       "46                 1.0           0.814833                 1.0      0.121699   \n",
       "47                 1.0           0.835583                 1.0      0.378941   \n",
       "48                 1.0           0.787611                 1.0      0.157689   \n",
       "49                 1.0           0.825624                 1.0      0.094723   \n",
       "50                 1.0           0.836820                 1.0      0.395849   \n",
       "51                 1.0           0.759779                 1.0      0.115147   \n",
       "52                 1.0           0.816842                 1.0      0.190041   \n",
       "53                 1.0           0.823909                 1.0      0.252769   \n",
       "\n",
       "    std_score_time  std_test_score  std_train_score  \n",
       "0         0.000235        0.016454     7.850462e-17  \n",
       "1         0.000085        0.015472     0.000000e+00  \n",
       "2         0.000236        0.017595     5.551115e-17  \n",
       "3         0.000022        0.001251     0.000000e+00  \n",
       "4         0.000190        0.013442     0.000000e+00  \n",
       "5         0.000086        0.003169     0.000000e+00  \n",
       "6         0.000156        0.019973     0.000000e+00  \n",
       "7         0.000149        0.015487     0.000000e+00  \n",
       "8         0.000256        0.005763     0.000000e+00  \n",
       "9         0.000254        0.009306     7.850462e-17  \n",
       "10        0.000222        0.009721     0.000000e+00  \n",
       "11        0.000088        0.015852     0.000000e+00  \n",
       "12        0.000160        0.004262     0.000000e+00  \n",
       "13        0.000144        0.009672     0.000000e+00  \n",
       "14        0.000115        0.008464     0.000000e+00  \n",
       "15        0.000132        0.021225     0.000000e+00  \n",
       "16        0.000306        0.017920     0.000000e+00  \n",
       "17        0.000129        0.013795     0.000000e+00  \n",
       "18        0.000133        0.017530     0.000000e+00  \n",
       "19        0.000073        0.010188     0.000000e+00  \n",
       "20        0.000178        0.013694     7.850462e-17  \n",
       "21        0.000060        0.010359     0.000000e+00  \n",
       "22        0.000132        0.010013     5.551115e-17  \n",
       "23        0.000101        0.015163     0.000000e+00  \n",
       "24        0.000118        0.018164     0.000000e+00  \n",
       "25        0.000104        0.016729     0.000000e+00  \n",
       "26        0.000070        0.011885     0.000000e+00  \n",
       "27        0.000061        0.014693     0.000000e+00  \n",
       "28        0.000174        0.011690     0.000000e+00  \n",
       "29        0.000168        0.009115     0.000000e+00  \n",
       "30        0.000119        0.014634     0.000000e+00  \n",
       "31        0.000187        0.008745     0.000000e+00  \n",
       "32        0.000204        0.014220     0.000000e+00  \n",
       "33        0.000113        0.018767     0.000000e+00  \n",
       "34        0.000114        0.010876     0.000000e+00  \n",
       "35        0.000142        0.014503     0.000000e+00  \n",
       "36        0.000039        0.011098     0.000000e+00  \n",
       "37        0.000102        0.015354     0.000000e+00  \n",
       "38        0.000230        0.016054     0.000000e+00  \n",
       "39        0.000078        0.010029     0.000000e+00  \n",
       "40        0.000160        0.012024     0.000000e+00  \n",
       "41        0.000101        0.017047     0.000000e+00  \n",
       "42        0.000176        0.018997     0.000000e+00  \n",
       "43        0.000082        0.016670     0.000000e+00  \n",
       "44        0.000337        0.011853     0.000000e+00  \n",
       "45        0.000183        0.017283     0.000000e+00  \n",
       "46        0.000104        0.010336     0.000000e+00  \n",
       "47        0.000209        0.006137     0.000000e+00  \n",
       "48        0.000184        0.012477     7.850462e-17  \n",
       "49        0.000244        0.007811     5.551115e-17  \n",
       "50        0.000244        0.010400     0.000000e+00  \n",
       "51        0.000068        0.026617     0.000000e+00  \n",
       "52        0.000198        0.016198     0.000000e+00  \n",
       "53        0.000305        0.012563     0.000000e+00  \n",
       "\n",
       "[54 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Pickle created----\n"
     ]
    }
   ],
   "source": [
    "# Creation of the pickle which is goig to store the model.\n",
    "\n",
    "pickle_out = open(\"RF_roc_auc_HOG_2.pickle\",\"wb\") #Modify with the name of the dataset you're saving\n",
    "pickle.dump(gs.cv_results_, pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "print(\"----Pickle created----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = gs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'bootstrap': False, 'class_weight': 'balanced', 'max_depth': 50, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Best roc_auc score: 0.848984\n"
     ]
    }
   ],
   "source": [
    "print(\"Best hyperparameters: {}\".format(gs.best_params_))\n",
    "print(\"Best roc_auc score: {:3f}\".format(gs.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "GridSearchCV(cv=StratifiedKFold(n_splits=4, random_state=31, shuffle=True),\n",
      "       error_score='raise',\n",
      "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'n_estimators': [50, 100, 200], 'max_depth': [50, 60, 70], 'min_samples_split': [2, 5, 10], 'bootstrap': [True, False], 'class_weight': ['balanced']},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "       scoring='roc_auc', verbose=3):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.59      0.84      0.69       480\n",
      "          1       0.66      0.34      0.45       432\n",
      "\n",
      "avg / total       0.62      0.61      0.58       912\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(gs, metrics.classification_report(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End time:  2019-01-16 15:23:41.745017\n",
      "Uptime : 3:52:34.234460\n"
     ]
    }
   ],
   "source": [
    "# End point\n",
    "end_time = time.time()\n",
    "uptime = end_time - start_time\n",
    "human_uptime = datetime.timedelta(seconds=uptime)\n",
    "\n",
    "print(\"End time: \", datetime.datetime.now())\n",
    "print(\"Uptime :\" ,human_uptime)"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}