Image_Classification_DAISY_3_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 16:39:16.035625\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_DAISY_110_3_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": {
    "collapsed": true
   },
   "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=   1.0s\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:    1.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   1.0s\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:    2.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   1.0s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.9s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.9s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.9s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.9s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   0.9s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   1.0s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l1, score=0.5, total=   1.0s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6022038567493113, total=   1.3s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.5636363636363637, total=   1.4s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6476584022038567, total=   1.5s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6528007346189165, total=   1.3s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.5451790633608815, total=   1.6s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.5696969696969697, total=   1.3s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.5425160697887971, total=   1.3s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6480257116620753, total=   1.4s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.5743801652892563, total=   1.4s\n",
      "[CV] C=0.01, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.01, class_weight=balanced, penalty=l2, score=0.6107438016528927, total=   1.2s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l1, score=0.5, total=   1.3s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l1 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.5470156106519743, total=   2.0s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.6577594123048669, total=   2.0s\n",
      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
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      "[CV] C=0.05, class_weight=balanced, penalty=l2 .......................\n",
      "[CV]  C=0.05, class_weight=balanced, penalty=l2, score=0.621763085399449, total=   2.1s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l1 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l1, score=0.5, total=   1.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.6348943985307621, total=   2.3s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.6059687786960515, total=   2.3s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.6658402203856749, total=   2.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.6889807162534436, total=   2.2s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
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      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.1, class_weight=balanced, penalty=l2, score=0.594674012855831, total=   2.2s\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=0.6000918273645546, total=   2.3s\n",
      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
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      "[CV] C=0.1, class_weight=balanced, penalty=l2 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l1 ........................\n",
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      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6667584940312213, total=   3.7s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6476584022038568, total=   3.7s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6865013774104682, total=   3.0s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.7123966942148761, total=   3.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6046831955922866, total=   3.7s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6332415059687787, total=   3.1s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.642791551882461, total=   3.7s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6945821854912764, total=   3.0s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6422405876951331, total=   3.0s\n",
      "[CV] C=0.5, class_weight=balanced, penalty=l2 ........................\n",
      "[CV]  C=0.5, class_weight=balanced, penalty=l2, score=0.6564738292011021, total=   3.0s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.5552800734618917, total=   2.2s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.555463728191001, total=   2.2s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.5706152433425161, total=   2.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.6090909090909091, total=   2.7s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.5198347107438016, total=   3.4s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.5411386593204774, total=   2.1s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.4981634527089073, total=   2.3s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.5674012855831038, total=   2.4s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.556198347107438, total=   2.3s\n",
      "[CV] C=1, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l1, score=0.5798898071625345, total=   2.4s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6807162534435262, total=   3.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6669421487603305, total=   3.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6950413223140496, total=   4.3s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7223140495867769, total=   3.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6325068870523416, total=   3.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6492194674012857, total=   3.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.656749311294766, total=   3.6s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.7118457300275483, total=   3.5s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6493112947658402, total=   3.4s\n",
      "[CV] C=1, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=1, class_weight=balanced, penalty=l2, score=0.6762167125803491, total=   3.6s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6419651056014692, total=   2.8s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6514233241505969, total=   2.2s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.7025711662075298, total=   2.2s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.7429752066115702, total=   2.4s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.5837465564738292, total=   2.1s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.5861340679522499, total=   2.7s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.6039485766758494, total=   2.9s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.660238751147842, total=   2.7s\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=0.5987144168962351, total=   2.7s\n",
      "[CV] C=2, class_weight=balanced, penalty=l1 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l1, score=0.640771349862259, total=   2.1s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6934802571166209, total=   5.4s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6874196510560148, total=   4.7s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.707346189164371, total=   4.7s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7392102846648302, total=   4.5s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6559228650137741, total=   5.4s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6660238751147842, total=   5.4s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6764003673094583, total=   5.3s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.7256198347107439, total=   4.4s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6581267217630854, total=   4.4s\n",
      "[CV] C=2, class_weight=balanced, penalty=l2 ..........................\n",
      "[CV]  C=2, class_weight=balanced, penalty=l2, score=0.6970615243342516, total=   4.5s\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": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    .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>0.946181</td>\n",
       "      <td>0.009711</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>10</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>0.040070</td>\n",
       "      <td>0.017649</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.372397</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.595684</td>\n",
       "      <td>0.643196</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>7</td>\n",
       "      <td>0.602204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.648026</td>\n",
       "      <td>0.638755</td>\n",
       "      <td>0.574380</td>\n",
       "      <td>0.640721</td>\n",
       "      <td>0.610744</td>\n",
       "      <td>0.647974</td>\n",
       "      <td>0.111126</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.040628</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.207217</td>\n",
       "      <td>0.003725</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</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>10</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>0.025878</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.078948</td>\n",
       "      <td>0.004346</td>\n",
       "      <td>0.614040</td>\n",
       "      <td>0.661744</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>6</td>\n",
       "      <td>0.620569</td>\n",
       "      <td>...</td>\n",
       "      <td>0.657759</td>\n",
       "      <td>0.658226</td>\n",
       "      <td>0.602571</td>\n",
       "      <td>0.660876</td>\n",
       "      <td>0.621763</td>\n",
       "      <td>0.663724</td>\n",
       "      <td>0.140061</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.039103</td>\n",
       "      <td>0.003574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.180818</td>\n",
       "      <td>0.003749</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</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>0.500000</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>0.006263</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.190995</td>\n",
       "      <td>0.003585</td>\n",
       "      <td>0.625785</td>\n",
       "      <td>0.674796</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>5</td>\n",
       "      <td>0.634894</td>\n",
       "      <td>...</td>\n",
       "      <td>0.664187</td>\n",
       "      <td>0.671623</td>\n",
       "      <td>0.618916</td>\n",
       "      <td>0.675233</td>\n",
       "      <td>0.629568</td>\n",
       "      <td>0.675327</td>\n",
       "      <td>0.127370</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.037798</td>\n",
       "      <td>0.002882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.963071</td>\n",
       "      <td>0.003753</td>\n",
       "      <td>0.541690</td>\n",
       "      <td>0.543274</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>9</td>\n",
       "      <td>0.496327</td>\n",
       "      <td>...</td>\n",
       "      <td>0.547107</td>\n",
       "      <td>0.542225</td>\n",
       "      <td>0.519467</td>\n",
       "      <td>0.545555</td>\n",
       "      <td>0.568687</td>\n",
       "      <td>0.540414</td>\n",
       "      <td>0.132424</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.029943</td>\n",
       "      <td>0.003266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.300158</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.658733</td>\n",
       "      <td>0.718044</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>3</td>\n",
       "      <td>0.666758</td>\n",
       "      <td>...</td>\n",
       "      <td>0.694582</td>\n",
       "      <td>0.715023</td>\n",
       "      <td>0.642241</td>\n",
       "      <td>0.721813</td>\n",
       "      <td>0.656474</td>\n",
       "      <td>0.716261</td>\n",
       "      <td>0.337687</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.030374</td>\n",
       "      <td>0.002559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.444153</td>\n",
       "      <td>0.003769</td>\n",
       "      <td>0.555308</td>\n",
       "      <td>0.582827</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>0.555280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.567401</td>\n",
       "      <td>0.567555</td>\n",
       "      <td>0.556198</td>\n",
       "      <td>0.596791</td>\n",
       "      <td>0.579890</td>\n",
       "      <td>0.586202</td>\n",
       "      <td>0.358026</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.029331</td>\n",
       "      <td>0.018901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.655536</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.674086</td>\n",
       "      <td>0.741410</td>\n",
       "      <td>1</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 1, 'class_weight': 'balanced', 'penalty'...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.680716</td>\n",
       "      <td>...</td>\n",
       "      <td>0.711846</td>\n",
       "      <td>0.738100</td>\n",
       "      <td>0.649311</td>\n",
       "      <td>0.745669</td>\n",
       "      <td>0.676217</td>\n",
       "      <td>0.739423</td>\n",
       "      <td>0.241704</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.027481</td>\n",
       "      <td>0.002591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.476216</td>\n",
       "      <td>0.003855</td>\n",
       "      <td>0.641249</td>\n",
       "      <td>0.671564</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 2, 'class_weight': 'balanced', 'penalty'...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.641965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.660239</td>\n",
       "      <td>0.670096</td>\n",
       "      <td>0.598714</td>\n",
       "      <td>0.671867</td>\n",
       "      <td>0.640771</td>\n",
       "      <td>0.674991</td>\n",
       "      <td>0.288524</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.049110</td>\n",
       "      <td>0.005312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.871757</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.690661</td>\n",
       "      <td>0.767342</td>\n",
       "      <td>2</td>\n",
       "      <td>balanced</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 2, 'class_weight': 'balanced', 'penalty'...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.693480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725620</td>\n",
       "      <td>0.764263</td>\n",
       "      <td>0.658127</td>\n",
       "      <td>0.771013</td>\n",
       "      <td>0.697062</td>\n",
       "      <td>0.765301</td>\n",
       "      <td>0.429633</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.026443</td>\n",
       "      <td>0.002515</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        0.946181         0.009711         0.500000          0.500000    0.01   \n",
       "1        1.372397         0.003800         0.595684          0.643196    0.01   \n",
       "2        1.207217         0.003725         0.500000          0.500000    0.05   \n",
       "3        2.078948         0.004346         0.614040          0.661744    0.05   \n",
       "4        1.180818         0.003749         0.500000          0.500000     0.1   \n",
       "5        2.190995         0.003585         0.625785          0.674796     0.1   \n",
       "6        1.963071         0.003753         0.541690          0.543274     0.5   \n",
       "7        3.300158         0.003576         0.658733          0.718044     0.5   \n",
       "8        2.444153         0.003769         0.555308          0.582827       1   \n",
       "9        3.655536         0.003576         0.674086          0.741410       1   \n",
       "10       2.476216         0.003855         0.641249          0.671564       2   \n",
       "11       4.871757         0.003581         0.690661          0.767342       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...               10   \n",
       "1   {'C': 0.01, 'class_weight': 'balanced', 'penal...                7   \n",
       "2   {'C': 0.05, 'class_weight': 'balanced', 'penal...               10   \n",
       "3   {'C': 0.05, 'class_weight': 'balanced', 'penal...                6   \n",
       "4   {'C': 0.1, 'class_weight': 'balanced', 'penalt...               10   \n",
       "5   {'C': 0.1, 'class_weight': 'balanced', 'penalt...                5   \n",
       "6   {'C': 0.5, 'class_weight': 'balanced', 'penalt...                9   \n",
       "7   {'C': 0.5, 'class_weight': 'balanced', 'penalt...                3   \n",
       "8   {'C': 1, 'class_weight': 'balanced', 'penalty'...                8   \n",
       "9   {'C': 1, 'class_weight': 'balanced', 'penalty'...                2   \n",
       "10  {'C': 2, 'class_weight': 'balanced', 'penalty'...                4   \n",
       "11  {'C': 2, 'class_weight': 'balanced', 'penalty'...                1   \n",
       "\n",
       "    split0_test_score       ...         split7_test_score  split7_train_score  \\\n",
       "0            0.500000       ...                  0.500000            0.500000   \n",
       "1            0.602204       ...                  0.648026            0.638755   \n",
       "2            0.500000       ...                  0.500000            0.500000   \n",
       "3            0.620569       ...                  0.657759            0.658226   \n",
       "4            0.500000       ...                  0.500000            0.500000   \n",
       "5            0.634894       ...                  0.664187            0.671623   \n",
       "6            0.496327       ...                  0.547107            0.542225   \n",
       "7            0.666758       ...                  0.694582            0.715023   \n",
       "8            0.555280       ...                  0.567401            0.567555   \n",
       "9            0.680716       ...                  0.711846            0.738100   \n",
       "10           0.641965       ...                  0.660239            0.670096   \n",
       "11           0.693480       ...                  0.725620            0.764263   \n",
       "\n",
       "    split8_test_score  split8_train_score  split9_test_score  \\\n",
       "0            0.500000            0.500000           0.500000   \n",
       "1            0.574380            0.640721           0.610744   \n",
       "2            0.500000            0.500000           0.500000   \n",
       "3            0.602571            0.660876           0.621763   \n",
       "4            0.500000            0.500000           0.500000   \n",
       "5            0.618916            0.675233           0.629568   \n",
       "6            0.519467            0.545555           0.568687   \n",
       "7            0.642241            0.721813           0.656474   \n",
       "8            0.556198            0.596791           0.579890   \n",
       "9            0.649311            0.745669           0.676217   \n",
       "10           0.598714            0.671867           0.640771   \n",
       "11           0.658127            0.771013           0.697062   \n",
       "\n",
       "    split9_train_score  std_fit_time  std_score_time  std_test_score  \\\n",
       "0             0.500000      0.040070        0.017649        0.000000   \n",
       "1             0.647974      0.111126        0.000488        0.040628   \n",
       "2             0.500000      0.025878        0.000041        0.000000   \n",
       "3             0.663724      0.140061        0.001627        0.039103   \n",
       "4             0.500000      0.006263        0.000028        0.000000   \n",
       "5             0.675327      0.127370        0.000023        0.037798   \n",
       "6             0.540414      0.132424        0.000012        0.029943   \n",
       "7             0.716261      0.337687        0.000012        0.030374   \n",
       "8             0.586202      0.358026        0.000028        0.029331   \n",
       "9             0.739423      0.241704        0.000014        0.027481   \n",
       "10            0.674991      0.288524        0.000179        0.049110   \n",
       "11            0.765301      0.429633        0.000031        0.026443   \n",
       "\n",
       "    std_train_score  \n",
       "0          0.000000  \n",
       "1          0.005479  \n",
       "2          0.000000  \n",
       "3          0.003574  \n",
       "4          0.000000  \n",
       "5          0.002882  \n",
       "6          0.003266  \n",
       "7          0.002559  \n",
       "8          0.018901  \n",
       "9          0.002591  \n",
       "10         0.005312  \n",
       "11         0.002515  \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_DAISY_3.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': 2, 'class_weight': 'balanced', 'penalty': 'l2'}\n",
      "Best roc_auc score: 0.690661\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": "raw",
   "metadata": {},
   "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": {
    "scrolled": true
   },
   "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=0.5684310850439882, total= 1.2min\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:  2.2min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.6141055718475072, total= 1.2min\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:  4.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, class_weight=balanced, gamma=0.001, kernel=rbf, score=0.5433640044166359, total= 1.2min\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=0.601648877438351, total= 1.3min\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=0.5681378299120234, total= 1.2min\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=0.6141202346041056, total= 1.2min\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=0.5433051159366948, total= 1.3min\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=0.601324990798675, total= 1.3min\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=0.5684310850439882, total= 1.2min\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=0.6141055718475072, total= 1.2min\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=0.5433640044166359, total= 1.2min\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=0.601648877438351, total= 1.2min\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=0.5681378299120234, total= 1.2min\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=0.6141202346041056, total= 1.2min\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=0.5433051159366948, total= 1.2min\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=0.601324990798675, total= 1.2min\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=0.6149853372434018, total= 1.2min\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=0.6422140762463344, total= 1.2min\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=0.6099521531100478, total= 1.2min\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=0.6326978284873022, total= 1.2min\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=0.5681378299120234, total= 1.2min\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=0.6141202346041056, total= 1.2min\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=0.5433051159366948, total= 1.2min\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=0.601324990798675, total= 1.2min\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=0.699076246334311, total= 1.1min\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=0.7021994134897361, total= 1.1min\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=0.6690025763709975, total= 1.1min\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=0.6995509753404491, total= 1.1min\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=0.614633431085044, total= 1.2min\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=0.6420821114369502, total= 1.2min\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=0.6097902097902098, total= 1.2min\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=0.6324475524475525, total= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed: 66.9min 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": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</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>55.831800</td>\n",
       "      <td>18.500885</td>\n",
       "      <td>0.581896</td>\n",
       "      <td>0.634364</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>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614106</td>\n",
       "      <td>0.637373</td>\n",
       "      <td>0.543364</td>\n",
       "      <td>0.635972</td>\n",
       "      <td>0.601649</td>\n",
       "      <td>0.629794</td>\n",
       "      <td>0.807686</td>\n",
       "      <td>0.296982</td>\n",
       "      <td>0.027807</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.768690</td>\n",
       "      <td>18.975788</td>\n",
       "      <td>0.581731</td>\n",
       "      <td>0.634216</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>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614120</td>\n",
       "      <td>0.637272</td>\n",
       "      <td>0.543305</td>\n",
       "      <td>0.635755</td>\n",
       "      <td>0.601325</td>\n",
       "      <td>0.629650</td>\n",
       "      <td>0.958382</td>\n",
       "      <td>0.515612</td>\n",
       "      <td>0.027811</td>\n",
       "      <td>0.002853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.044163</td>\n",
       "      <td>18.266336</td>\n",
       "      <td>0.581896</td>\n",
       "      <td>0.634364</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>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614106</td>\n",
       "      <td>0.637373</td>\n",
       "      <td>0.543364</td>\n",
       "      <td>0.635972</td>\n",
       "      <td>0.601649</td>\n",
       "      <td>0.629794</td>\n",
       "      <td>0.492905</td>\n",
       "      <td>0.046947</td>\n",
       "      <td>0.027807</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.195572</td>\n",
       "      <td>18.371802</td>\n",
       "      <td>0.581731</td>\n",
       "      <td>0.634216</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>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614120</td>\n",
       "      <td>0.637272</td>\n",
       "      <td>0.543305</td>\n",
       "      <td>0.635755</td>\n",
       "      <td>0.601325</td>\n",
       "      <td>0.629650</td>\n",
       "      <td>0.369913</td>\n",
       "      <td>0.256913</td>\n",
       "      <td>0.027811</td>\n",
       "      <td>0.002853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.262908</td>\n",
       "      <td>17.545612</td>\n",
       "      <td>0.624966</td>\n",
       "      <td>0.679156</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>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.642214</td>\n",
       "      <td>0.675539</td>\n",
       "      <td>0.609952</td>\n",
       "      <td>0.693030</td>\n",
       "      <td>0.632698</td>\n",
       "      <td>0.671073</td>\n",
       "      <td>0.129788</td>\n",
       "      <td>0.078148</td>\n",
       "      <td>0.013062</td>\n",
       "      <td>0.008301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54.672679</td>\n",
       "      <td>18.174279</td>\n",
       "      <td>0.581731</td>\n",
       "      <td>0.634216</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>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614120</td>\n",
       "      <td>0.637272</td>\n",
       "      <td>0.543305</td>\n",
       "      <td>0.635755</td>\n",
       "      <td>0.601325</td>\n",
       "      <td>0.629650</td>\n",
       "      <td>0.027076</td>\n",
       "      <td>0.017069</td>\n",
       "      <td>0.027811</td>\n",
       "      <td>0.002853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48.423498</td>\n",
       "      <td>15.470352</td>\n",
       "      <td>0.692465</td>\n",
       "      <td>0.784700</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>0.702199</td>\n",
       "      <td>0.781575</td>\n",
       "      <td>0.669003</td>\n",
       "      <td>0.793352</td>\n",
       "      <td>0.699551</td>\n",
       "      <td>0.781177</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.062799</td>\n",
       "      <td>0.013590</td>\n",
       "      <td>0.005026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>53.206612</td>\n",
       "      <td>17.495161</td>\n",
       "      <td>0.624742</td>\n",
       "      <td>0.678943</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>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.642082</td>\n",
       "      <td>0.675277</td>\n",
       "      <td>0.609790</td>\n",
       "      <td>0.692805</td>\n",
       "      <td>0.632448</td>\n",
       "      <td>0.670822</td>\n",
       "      <td>0.188395</td>\n",
       "      <td>0.047116</td>\n",
       "      <td>0.013095</td>\n",
       "      <td>0.008304</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      55.831800        18.500885         0.581896          0.634364       1   \n",
       "1      56.768690        18.975788         0.581731          0.634216       1   \n",
       "2      55.044163        18.266336         0.581896          0.634364      10   \n",
       "3      55.195572        18.371802         0.581731          0.634216      10   \n",
       "4      53.262908        17.545612         0.624966          0.679156     100   \n",
       "5      54.672679        18.174279         0.581731          0.634216     100   \n",
       "6      48.423498        15.470352         0.692465          0.784700    1000   \n",
       "7      53.206612        17.495161         0.624742          0.678943    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': ...                4   \n",
       "1  {'C': 1, 'class_weight': 'balanced', 'gamma': ...                6   \n",
       "2  {'C': 10, 'class_weight': 'balanced', 'gamma':...                4   \n",
       "3  {'C': 10, 'class_weight': 'balanced', 'gamma':...                6   \n",
       "4  {'C': 100, 'class_weight': 'balanced', 'gamma'...                2   \n",
       "5  {'C': 100, 'class_weight': 'balanced', 'gamma'...                6   \n",
       "6  {'C': 1000, 'class_weight': 'balanced', 'gamma...                1   \n",
       "7  {'C': 1000, 'class_weight': 'balanced', 'gamma...                3   \n",
       "\n",
       "        ...         split1_test_score  split1_train_score  split2_test_score  \\\n",
       "0       ...                  0.614106            0.637373           0.543364   \n",
       "1       ...                  0.614120            0.637272           0.543305   \n",
       "2       ...                  0.614106            0.637373           0.543364   \n",
       "3       ...                  0.614120            0.637272           0.543305   \n",
       "4       ...                  0.642214            0.675539           0.609952   \n",
       "5       ...                  0.614120            0.637272           0.543305   \n",
       "6       ...                  0.702199            0.781575           0.669003   \n",
       "7       ...                  0.642082            0.675277           0.609790   \n",
       "\n",
       "   split2_train_score  split3_test_score  split3_train_score  std_fit_time  \\\n",
       "0            0.635972           0.601649            0.629794      0.807686   \n",
       "1            0.635755           0.601325            0.629650      0.958382   \n",
       "2            0.635972           0.601649            0.629794      0.492905   \n",
       "3            0.635755           0.601325            0.629650      0.369913   \n",
       "4            0.693030           0.632698            0.671073      0.129788   \n",
       "5            0.635755           0.601325            0.629650      0.027076   \n",
       "6            0.793352           0.699551            0.781177      0.077503   \n",
       "7            0.692805           0.632448            0.670822      0.188395   \n",
       "\n",
       "   std_score_time  std_test_score  std_train_score  \n",
       "0        0.296982        0.027807         0.002852  \n",
       "1        0.515612        0.027811         0.002853  \n",
       "2        0.046947        0.027807         0.002852  \n",
       "3        0.256913        0.027811         0.002853  \n",
       "4        0.078148        0.013062         0.008301  \n",
       "5        0.017069        0.027811         0.002853  \n",
       "6        0.062799        0.013590         0.005026  \n",
       "7        0.047116        0.013095         0.008304  \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_DAISY_3.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': 1000, 'class_weight': 'balanced', 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "Best roc_auc score: 0.692465\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       0.60      0.59      0.59       480\n",
      "          1       0.55      0.57      0.56       432\n",
      "\n",
      "avg / total       0.58      0.58      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": "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=0.9050586510263929, total=   4.2s\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:    4.3s 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=0.9062316715542521, total=   4.2s\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:    8.6s 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=0.8953698932646301, total=   4.2s\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=0.8785645933014354, total=   4.2s\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=0.913357771260997, total=   8.3s\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=0.9292595307917888, total=   8.1s\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=0.9070813397129186, total=   8.3s\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=0.8991682002208318, total=   8.5s\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=0.9199706744868035, total=  16.3s\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=0.9211217008797654, total=  16.3s\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=0.9122561648877439, total=  16.4s\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=0.9015237394184763, total=  16.6s\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=0.9014882697947214, total=   4.3s\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=0.9144354838709678, total=   4.1s\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=0.9007287449392711, total=   4.2s\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=0.890842841369157, total=   4.2s\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=0.9160850439882698, total=   8.2s\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=0.9240615835777126, total=   8.2s\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=0.9080456385719544, total=   8.3s\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=0.9038498343761502, total=   8.5s\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=0.9155718475073313, total=  16.4s\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=0.9260850439882699, total=  16.2s\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=0.9123297754876702, total=  16.5s\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=0.9057048214942952, total=  16.6s\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=0.9064222873900293, total=   4.2s\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=0.8999853372434017, total=   4.1s\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=0.8985793154214208, total=   4.2s\n",
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      "[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=0.9129032258064516, total=  15.9s\n",
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      "[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=0.9193035190615836, total=   4.3s\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=0.8970234604105571, total=   4.2s\n",
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      "[CV] bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n"
     ]
    },
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      "[CV]  bootstrap=True, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50, score=0.9049633431085045, total=   4.3s\n",
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      "[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=0.9182991202346041, total=  16.1s\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=0.9028634523371366, total=  16.4s\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=0.902569009937431, total=  16.7s\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=0.9321554252199413, total=   6.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=50 \n",
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      "[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=0.9119543614280458, total=   6.9s\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=0.9309310850439882, total=  13.1s\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=0.9329398826979473, total=  12.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=100 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=2, n_estimators=200 \n",
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      "[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=0.9371187683284457, total=  25.5s\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=0.935576002944424, total=  26.6s\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=0.9181891792418108, total=  26.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=50 \n",
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      "[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=0.9233934486566066, total=   6.7s\n",
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      "[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=0.9301906158357772, total=  13.3s\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=0.9298826979472141, total=  12.8s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=100 \n",
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      "[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=0.9091203533308796, total=  13.6s\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=0.9288123167155424, total=  25.9s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=5, n_estimators=200 \n",
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      "[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=0.9312992270887008, total=  26.4s\n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
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      "[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=0.9223460410557185, total=   6.5s\n",
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      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50, score=0.9247405226352594, total=   6.7s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=50 \n",
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      "[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=0.9270967741935484, total=  12.9s\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=0.9207184750733138, total=  12.6s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=100 \n",
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      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.931700879765396, total=  25.8s\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=0.9294721407624634, total=  25.2s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200 \n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=50, min_samples_split=10, n_estimators=200, score=0.9281413323518587, total=  26.1s\n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=50 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=100 \n",
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      "[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=0.929391495601173, total=  26.0s\n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=2, n_estimators=200 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=50 \n",
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      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100, score=0.9316788856304985, total=  13.0s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=100 \n",
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      "[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=0.9128965771071035, total=  13.4s\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=0.931041055718475, total=  26.0s\n",
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      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=5, n_estimators=200, score=0.9179683474420317, total=  26.6s\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=0.9181011730205278, total=   6.6s\n",
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      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=50, score=0.9249024659550975, total=   6.8s\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=0.911152005888848, total=   6.7s\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=0.9270821114369503, total=  13.0s\n",
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      "[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=0.9269782848730216, total=  13.3s\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=0.9129922708870079, total=  14.1s\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=0.9259677419354839, total=  26.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200, score=0.9266275659824047, total=  26.4s\n",
      "[CV] bootstrap=False, class_weight=balanced, max_depth=60, min_samples_split=10, n_estimators=200 \n",
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      "[CV] bootstrap=False, class_weight=balanced, max_depth=70, min_samples_split=2, n_estimators=50 \n",
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      "[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=0.9060434302539565, total=   7.0s\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=0.9338636363636363, total=  13.4s\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=0.9235850439882698, total=  14.3s\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=0.9282075818917924, total=  14.2s\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=0.9148619801251379, total=  14.6s\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=0.9286143695014663, total=  27.7s\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=0.9341568914956012, total=  26.0s\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=0.9268163415531836, total=  26.8s\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=0.9185351490614648, total=  28.6s\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=0.9231378299120234, total=   6.8s\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=0.9217302052785924, total=   6.5s\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=0.9241074714758927, total=   6.9s\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=0.9115495031284505, total=   6.8s\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=0.9255718475073313, total=  13.3s\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=0.9291642228739003, total=  12.9s\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=0.9311740890688259, total=  13.3s\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=0.9173279352226722, total=  13.6s\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=0.9281964809384166, total=  26.2s\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=0.9341788856304986, total=  25.6s\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=0.935149061464851, total=  26.8s\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=0.9158925285241075, total=  27.3s\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=0.9245747800586511, total=   6.5s\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=0.9185337243401759, total=   6.5s\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=0.9211777695988222, total=   6.7s\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=0.903467059256533, total=   6.7s\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=0.9286070381231671, total=  13.0s\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=0.9297067448680352, total=  12.6s\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=0.9274935590725065, total=  13.2s\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=0.9072506440927495, total=  13.5s\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=0.9251319648093841, total=  26.0s\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=0.9310703812316715, total=  25.4s\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=0.9276849466323152, total=  26.3s\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=0.9118586676481413, total=  26.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 216 out of 216 | elapsed: 45.5min 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": {
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "</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>4.198904</td>\n",
       "      <td>0.026231</td>\n",
       "      <td>0.896315</td>\n",
       "      <td>1.000000</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>0.906232</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895370</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.878565</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024908</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.011075</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.270871</td>\n",
       "      <td>0.034913</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>1.000000</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>0.929260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907081</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.899168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.150211</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.011051</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.325047</td>\n",
       "      <td>0.053191</td>\n",
       "      <td>0.913725</td>\n",
       "      <td>1.000000</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>0.921122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.912256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.901524</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.130817</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.007821</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.166206</td>\n",
       "      <td>0.026169</td>\n",
       "      <td>0.901880</td>\n",
       "      <td>1.000000</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>0.914435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900729</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890843</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.066610</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.245427</td>\n",
       "      <td>0.034728</td>\n",
       "      <td>0.913017</td>\n",
       "      <td>1.000000</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>0.924062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.908046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.903850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.110187</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.372784</td>\n",
       "      <td>0.052894</td>\n",
       "      <td>0.914929</td>\n",
       "      <td>1.000000</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>0.926085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.912330</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.905705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.142020</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.007361</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.163113</td>\n",
       "      <td>0.026148</td>\n",
       "      <td>0.896867</td>\n",
       "      <td>1.000000</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>0.899985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898579</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.882459</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.034871</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.008824</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.134492</td>\n",
       "      <td>0.034383</td>\n",
       "      <td>0.903540</td>\n",
       "      <td>1.000000</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>0.913211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.901715</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.889628</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.066945</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.009038</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.166167</td>\n",
       "      <td>0.052281</td>\n",
       "      <td>0.911062</td>\n",
       "      <td>1.000000</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>0.918343</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.913758</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.899227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.236236</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.007135</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.236400</td>\n",
       "      <td>0.026328</td>\n",
       "      <td>0.903317</td>\n",
       "      <td>1.000000</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>0.897023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.901980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.894943</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025706</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.009583</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.242228</td>\n",
       "      <td>0.034862</td>\n",
       "      <td>0.914058</td>\n",
       "      <td>1.000000</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>0.926232</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909960</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.905469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.085187</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.007731</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16.429921</td>\n",
       "      <td>0.053326</td>\n",
       "      <td>0.915666</td>\n",
       "      <td>1.000000</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>0.925403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.912477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.902621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.201377</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.008902</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.197206</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>0.904633</td>\n",
       "      <td>1.000000</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>0.922742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.891984</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.075675</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.012009</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.227130</td>\n",
       "      <td>0.034726</td>\n",
       "      <td>0.908806</td>\n",
       "      <td>1.000000</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>0.914971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906515</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.899934</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.042174</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.006056</td>\n",
       "      <td>7.850462e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16.296543</td>\n",
       "      <td>0.052790</td>\n",
       "      <td>0.915606</td>\n",
       "      <td>1.000000</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>0.922595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.915789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900670</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.186358</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.009107</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.140045</td>\n",
       "      <td>0.026211</td>\n",
       "      <td>0.898998</td>\n",
       "      <td>1.000000</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>0.906804</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.897343</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.875098</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.053734</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.015396</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.098125</td>\n",
       "      <td>0.034427</td>\n",
       "      <td>0.907431</td>\n",
       "      <td>1.000000</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>0.917287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910151</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.891719</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.088081</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.009499</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.291620</td>\n",
       "      <td>0.052480</td>\n",
       "      <td>0.909982</td>\n",
       "      <td>1.000000</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>0.921891</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906765</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.897004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.189733</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.009202</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.221793</td>\n",
       "      <td>0.026335</td>\n",
       "      <td>0.899094</td>\n",
       "      <td>1.000000</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>0.914311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.887751</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.889312</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.040711</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.340019</td>\n",
       "      <td>0.034945</td>\n",
       "      <td>0.911842</td>\n",
       "      <td>1.000000</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>0.913248</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910217</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.906367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.063138</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.004088</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16.396520</td>\n",
       "      <td>0.053763</td>\n",
       "      <td>0.917118</td>\n",
       "      <td>1.000000</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>0.927705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.913927</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.905432</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.175357</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.008321</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.156645</td>\n",
       "      <td>0.026334</td>\n",
       "      <td>0.899463</td>\n",
       "      <td>1.000000</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>0.909787</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.890512</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.893669</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019311</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.007739</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.218346</td>\n",
       "      <td>0.034943</td>\n",
       "      <td>0.908656</td>\n",
       "      <td>1.000000</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>0.906686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909856</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890453</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.132502</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.013185</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.383828</td>\n",
       "      <td>0.053367</td>\n",
       "      <td>0.913930</td>\n",
       "      <td>1.000000</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>0.915543</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.904954</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137801</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.006803</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.153390</td>\n",
       "      <td>0.026257</td>\n",
       "      <td>0.893575</td>\n",
       "      <td>0.999998</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>0.901085</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.889658</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.882429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.035525</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.007946</td>\n",
       "      <td>2.829455e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8.156929</td>\n",
       "      <td>0.034708</td>\n",
       "      <td>0.906640</td>\n",
       "      <td>1.000000</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>0.923211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.901303</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.889967</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.086070</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.012351</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16.260590</td>\n",
       "      <td>0.055083</td>\n",
       "      <td>0.910086</td>\n",
       "      <td>1.000000</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>0.918299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.902569</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.254067</td>\n",
       "      <td>0.003982</td>\n",
       "      <td>0.007388</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6.719623</td>\n",
       "      <td>0.026966</td>\n",
       "      <td>0.925717</td>\n",
       "      <td>1.000000</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>0.932742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925992</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.911954</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.148048</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.008370</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13.252678</td>\n",
       "      <td>0.036267</td>\n",
       "      <td>0.926924</td>\n",
       "      <td>1.000000</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>0.932940</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.915848</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.378355</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.006632</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26.222678</td>\n",
       "      <td>0.055839</td>\n",
       "      <td>0.930732</td>\n",
       "      <td>1.000000</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>0.937119</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935576</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.918189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.522611</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.007469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>6.615112</td>\n",
       "      <td>0.026782</td>\n",
       "      <td>0.916856</td>\n",
       "      <td>1.000000</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>0.919091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.923393</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.909790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.238510</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.005012</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13.243246</td>\n",
       "      <td>0.035877</td>\n",
       "      <td>0.925468</td>\n",
       "      <td>1.000000</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>0.929883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.932661</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.909120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.285332</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.009494</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>25.958395</td>\n",
       "      <td>0.054828</td>\n",
       "      <td>0.926944</td>\n",
       "      <td>1.000000</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>0.931979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.915672</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545582</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.006610</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6.579883</td>\n",
       "      <td>0.026308</td>\n",
       "      <td>0.918598</td>\n",
       "      <td>1.000000</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>0.922346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.906941</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167336</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.006903</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>12.993492</td>\n",
       "      <td>0.035151</td>\n",
       "      <td>0.922665</td>\n",
       "      <td>1.000000</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>0.920718</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927302</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.915539</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.270929</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>25.878334</td>\n",
       "      <td>0.053808</td>\n",
       "      <td>0.925394</td>\n",
       "      <td>1.000000</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>0.929472</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928141</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.912241</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.496237</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.007695</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6.666640</td>\n",
       "      <td>0.026499</td>\n",
       "      <td>0.919012</td>\n",
       "      <td>1.000000</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>0.918548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.922694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.914884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200149</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.002811</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>13.151888</td>\n",
       "      <td>0.035505</td>\n",
       "      <td>0.924969</td>\n",
       "      <td>1.000000</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>0.930301</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930401</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.913993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.281716</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.006677</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>26.235862</td>\n",
       "      <td>0.054817</td>\n",
       "      <td>0.927459</td>\n",
       "      <td>1.000000</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>0.930235</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.918395</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.508339</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.005301</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>6.598020</td>\n",
       "      <td>0.026426</td>\n",
       "      <td>0.921650</td>\n",
       "      <td>1.000000</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>0.928666</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.913088</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.140819</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.006026</td>\n",
       "      <td>5.551115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>13.012858</td>\n",
       "      <td>0.035497</td>\n",
       "      <td>0.926728</td>\n",
       "      <td>1.000000</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>0.930477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931844</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.912897</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.254189</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.007998</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>26.005479</td>\n",
       "      <td>0.053867</td>\n",
       "      <td>0.928400</td>\n",
       "      <td>1.000000</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>0.932419</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.932160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.917968</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.524060</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>6.608699</td>\n",
       "      <td>0.026245</td>\n",
       "      <td>0.918539</td>\n",
       "      <td>1.000000</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>0.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.911152</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.113240</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.004932</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>13.229600</td>\n",
       "      <td>0.035576</td>\n",
       "      <td>0.923456</td>\n",
       "      <td>1.000000</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>0.926760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926978</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.912992</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.512126</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.006039</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>27.067865</td>\n",
       "      <td>0.057266</td>\n",
       "      <td>0.924836</td>\n",
       "      <td>1.000000</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>0.926628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930085</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916658</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.724988</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.004971</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6.795345</td>\n",
       "      <td>0.028281</td>\n",
       "      <td>0.916804</td>\n",
       "      <td>1.000000</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>0.925594</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916238</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.906043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137017</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>14.068016</td>\n",
       "      <td>0.036511</td>\n",
       "      <td>0.925133</td>\n",
       "      <td>1.000000</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>0.923585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.914862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.440925</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.006956</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>27.231124</td>\n",
       "      <td>0.059555</td>\n",
       "      <td>0.927035</td>\n",
       "      <td>1.000000</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>0.934157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.926816</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.918535</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995767</td>\n",
       "      <td>0.003466</td>\n",
       "      <td>0.005601</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6.743792</td>\n",
       "      <td>0.027047</td>\n",
       "      <td>0.920133</td>\n",
       "      <td>1.000000</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>0.921730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.911550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.179329</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.005024</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13.211703</td>\n",
       "      <td>0.036137</td>\n",
       "      <td>0.925811</td>\n",
       "      <td>1.000000</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>0.929164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931174</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.917328</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.245677</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.005290</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>26.389754</td>\n",
       "      <td>0.056118</td>\n",
       "      <td>0.928357</td>\n",
       "      <td>1.000000</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>0.934179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935149</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.915893</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.644720</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.007669</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.566455</td>\n",
       "      <td>0.026751</td>\n",
       "      <td>0.916943</td>\n",
       "      <td>1.000000</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>0.918534</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921178</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.903467</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.099155</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>13.036775</td>\n",
       "      <td>0.035835</td>\n",
       "      <td>0.923270</td>\n",
       "      <td>1.000000</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>0.929707</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927494</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.907251</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.334664</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.009276</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>26.092228</td>\n",
       "      <td>0.055122</td>\n",
       "      <td>0.923940</td>\n",
       "      <td>1.000000</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>0.931070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927685</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.911859</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.524082</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.007283</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        4.198904         0.026231         0.896315          1.000000   \n",
       "1        8.270871         0.034913         0.912225          1.000000   \n",
       "2       16.325047         0.053191         0.913725          1.000000   \n",
       "3        4.166206         0.026169         0.901880          1.000000   \n",
       "4        8.245427         0.034728         0.913017          1.000000   \n",
       "5       16.372784         0.052894         0.914929          1.000000   \n",
       "6        4.163113         0.026148         0.896867          1.000000   \n",
       "7        8.134492         0.034383         0.903540          1.000000   \n",
       "8       16.166167         0.052281         0.911062          1.000000   \n",
       "9        4.236400         0.026328         0.903317          1.000000   \n",
       "10       8.242228         0.034862         0.914058          1.000000   \n",
       "11      16.429921         0.053326         0.915666          1.000000   \n",
       "12       4.197206         0.026226         0.904633          1.000000   \n",
       "13       8.227130         0.034726         0.908806          1.000000   \n",
       "14      16.296543         0.052790         0.915606          1.000000   \n",
       "15       4.140045         0.026211         0.898998          1.000000   \n",
       "16       8.098125         0.034427         0.907431          1.000000   \n",
       "17      16.291620         0.052480         0.909982          1.000000   \n",
       "18       4.221793         0.026335         0.899094          1.000000   \n",
       "19       8.340019         0.034945         0.911842          1.000000   \n",
       "20      16.396520         0.053763         0.917118          1.000000   \n",
       "21       4.156645         0.026334         0.899463          1.000000   \n",
       "22       8.218346         0.034943         0.908656          1.000000   \n",
       "23      16.383828         0.053367         0.913930          1.000000   \n",
       "24       4.153390         0.026257         0.893575          0.999998   \n",
       "25       8.156929         0.034708         0.906640          1.000000   \n",
       "26      16.260590         0.055083         0.910086          1.000000   \n",
       "27       6.719623         0.026966         0.925717          1.000000   \n",
       "28      13.252678         0.036267         0.926924          1.000000   \n",
       "29      26.222678         0.055839         0.930732          1.000000   \n",
       "30       6.615112         0.026782         0.916856          1.000000   \n",
       "31      13.243246         0.035877         0.925468          1.000000   \n",
       "32      25.958395         0.054828         0.926944          1.000000   \n",
       "33       6.579883         0.026308         0.918598          1.000000   \n",
       "34      12.993492         0.035151         0.922665          1.000000   \n",
       "35      25.878334         0.053808         0.925394          1.000000   \n",
       "36       6.666640         0.026499         0.919012          1.000000   \n",
       "37      13.151888         0.035505         0.924969          1.000000   \n",
       "38      26.235862         0.054817         0.927459          1.000000   \n",
       "39       6.598020         0.026426         0.921650          1.000000   \n",
       "40      13.012858         0.035497         0.926728          1.000000   \n",
       "41      26.005479         0.053867         0.928400          1.000000   \n",
       "42       6.608699         0.026245         0.918539          1.000000   \n",
       "43      13.229600         0.035576         0.923456          1.000000   \n",
       "44      27.067865         0.057266         0.924836          1.000000   \n",
       "45       6.795345         0.028281         0.916804          1.000000   \n",
       "46      14.068016         0.036511         0.925133          1.000000   \n",
       "47      27.231124         0.059555         0.927035          1.000000   \n",
       "48       6.743792         0.027047         0.920133          1.000000   \n",
       "49      13.211703         0.036137         0.925811          1.000000   \n",
       "50      26.389754         0.056118         0.928357          1.000000   \n",
       "51       6.566455         0.026751         0.916943          1.000000   \n",
       "52      13.036775         0.035835         0.923270          1.000000   \n",
       "53      26.092228         0.055122         0.923940          1.000000   \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        ...                  0.906232            1.000000           0.895370   \n",
       "1        ...                  0.929260            1.000000           0.907081   \n",
       "2        ...                  0.921122            1.000000           0.912256   \n",
       "3        ...                  0.914435            1.000000           0.900729   \n",
       "4        ...                  0.924062            1.000000           0.908046   \n",
       "5        ...                  0.926085            1.000000           0.912330   \n",
       "6        ...                  0.899985            1.000000           0.898579   \n",
       "7        ...                  0.913211            1.000000           0.901715   \n",
       "8        ...                  0.918343            1.000000           0.913758   \n",
       "9        ...                  0.897023            1.000000           0.901980   \n",
       "10       ...                  0.926232            1.000000           0.909960   \n",
       "11       ...                  0.925403            1.000000           0.912477   \n",
       "12       ...                  0.922742            1.000000           0.895812   \n",
       "13       ...                  0.914971            1.000000           0.906515   \n",
       "14       ...                  0.922595            1.000000           0.915789   \n",
       "15       ...                  0.906804            1.000000           0.897343   \n",
       "16       ...                  0.917287            1.000000           0.910151   \n",
       "17       ...                  0.921891            1.000000           0.906765   \n",
       "18       ...                  0.914311            1.000000           0.887751   \n",
       "19       ...                  0.913248            1.000000           0.910217   \n",
       "20       ...                  0.927705            1.000000           0.913927   \n",
       "21       ...                  0.909787            1.000000           0.890512   \n",
       "22       ...                  0.906686            1.000000           0.909856   \n",
       "23       ...                  0.915543            1.000000           0.911476   \n",
       "24       ...                  0.901085            0.999993           0.889658   \n",
       "25       ...                  0.923211            1.000000           0.901303   \n",
       "26       ...                  0.918299            1.000000           0.902863   \n",
       "27       ...                  0.932742            1.000000           0.925992   \n",
       "28       ...                  0.932940            1.000000           0.927957   \n",
       "29       ...                  0.937119            1.000000           0.935576   \n",
       "30       ...                  0.919091            1.000000           0.923393   \n",
       "31       ...                  0.929883            1.000000           0.932661   \n",
       "32       ...                  0.931979            1.000000           0.931299   \n",
       "33       ...                  0.922346            1.000000           0.924741   \n",
       "34       ...                  0.920718            1.000000           0.927302   \n",
       "35       ...                  0.929472            1.000000           0.928141   \n",
       "36       ...                  0.918548            1.000000           0.922694   \n",
       "37       ...                  0.930301            1.000000           0.930401   \n",
       "38       ...                  0.930235            1.000000           0.931807   \n",
       "39       ...                  0.928666            1.000000           0.925705   \n",
       "40       ...                  0.930477            1.000000           0.931844   \n",
       "41       ...                  0.932419            1.000000           0.932160   \n",
       "42       ...                  0.920000            1.000000           0.924902   \n",
       "43       ...                  0.926760            1.000000           0.926978   \n",
       "44       ...                  0.926628            1.000000           0.930085   \n",
       "45       ...                  0.925594            1.000000           0.916238   \n",
       "46       ...                  0.923585            1.000000           0.928208   \n",
       "47       ...                  0.934157            1.000000           0.926816   \n",
       "48       ...                  0.921730            1.000000           0.924107   \n",
       "49       ...                  0.929164            1.000000           0.931174   \n",
       "50       ...                  0.934179            1.000000           0.935149   \n",
       "51       ...                  0.918534            1.000000           0.921178   \n",
       "52       ...                  0.929707            1.000000           0.927494   \n",
       "53       ...                  0.931070            1.000000           0.927685   \n",
       "\n",
       "    split2_train_score  split3_test_score  split3_train_score  std_fit_time  \\\n",
       "0                  1.0           0.878565                 1.0      0.024908   \n",
       "1                  1.0           0.899168                 1.0      0.150211   \n",
       "2                  1.0           0.901524                 1.0      0.130817   \n",
       "3                  1.0           0.890843                 1.0      0.066610   \n",
       "4                  1.0           0.903850                 1.0      0.110187   \n",
       "5                  1.0           0.905705                 1.0      0.142020   \n",
       "6                  1.0           0.882459                 1.0      0.034871   \n",
       "7                  1.0           0.889628                 1.0      0.066945   \n",
       "8                  1.0           0.899227                 1.0      0.236236   \n",
       "9                  1.0           0.894943                 1.0      0.025706   \n",
       "10                 1.0           0.905469                 1.0      0.085187   \n",
       "11                 1.0           0.902621                 1.0      0.201377   \n",
       "12                 1.0           0.891984                 1.0      0.075675   \n",
       "13                 1.0           0.899934                 1.0      0.042174   \n",
       "14                 1.0           0.900670                 1.0      0.186358   \n",
       "15                 1.0           0.875098                 1.0      0.053734   \n",
       "16                 1.0           0.891719                 1.0      0.088081   \n",
       "17                 1.0           0.897004                 1.0      0.189733   \n",
       "18                 1.0           0.889312                 1.0      0.040711   \n",
       "19                 1.0           0.906367                 1.0      0.063138   \n",
       "20                 1.0           0.905432                 1.0      0.175357   \n",
       "21                 1.0           0.893669                 1.0      0.019311   \n",
       "22                 1.0           0.890453                 1.0      0.132502   \n",
       "23                 1.0           0.904954                 1.0      0.137801   \n",
       "24                 1.0           0.882429                 1.0      0.035525   \n",
       "25                 1.0           0.889967                 1.0      0.086070   \n",
       "26                 1.0           0.902569                 1.0      0.254067   \n",
       "27                 1.0           0.911954                 1.0      0.148048   \n",
       "28                 1.0           0.915848                 1.0      0.378355   \n",
       "29                 1.0           0.918189                 1.0      0.522611   \n",
       "30                 1.0           0.909790                 1.0      0.238510   \n",
       "31                 1.0           0.909120                 1.0      0.285332   \n",
       "32                 1.0           0.915672                 1.0      0.545582   \n",
       "33                 1.0           0.906941                 1.0      0.167336   \n",
       "34                 1.0           0.915539                 1.0      0.270929   \n",
       "35                 1.0           0.912241                 1.0      0.496237   \n",
       "36                 1.0           0.914884                 1.0      0.200149   \n",
       "37                 1.0           0.913993                 1.0      0.281716   \n",
       "38                 1.0           0.918395                 1.0      0.508339   \n",
       "39                 1.0           0.913088                 1.0      0.140819   \n",
       "40                 1.0           0.912897                 1.0      0.254189   \n",
       "41                 1.0           0.917968                 1.0      0.524060   \n",
       "42                 1.0           0.911152                 1.0      0.113240   \n",
       "43                 1.0           0.912992                 1.0      0.512126   \n",
       "44                 1.0           0.916658                 1.0      0.724988   \n",
       "45                 1.0           0.906043                 1.0      0.137017   \n",
       "46                 1.0           0.914862                 1.0      0.440925   \n",
       "47                 1.0           0.918535                 1.0      0.995767   \n",
       "48                 1.0           0.911550                 1.0      0.179329   \n",
       "49                 1.0           0.917328                 1.0      0.245677   \n",
       "50                 1.0           0.915893                 1.0      0.644720   \n",
       "51                 1.0           0.903467                 1.0      0.099155   \n",
       "52                 1.0           0.907251                 1.0      0.334664   \n",
       "53                 1.0           0.911859                 1.0      0.524082   \n",
       "\n",
       "    std_score_time  std_test_score  std_train_score  \n",
       "0         0.000048        0.011075     0.000000e+00  \n",
       "1         0.000097        0.011051     5.551115e-17  \n",
       "2         0.000167        0.007821     0.000000e+00  \n",
       "3         0.000100        0.008381     5.551115e-17  \n",
       "4         0.000046        0.007749     0.000000e+00  \n",
       "5         0.000179        0.007361     0.000000e+00  \n",
       "6         0.000039        0.008824     0.000000e+00  \n",
       "7         0.000042        0.009038     0.000000e+00  \n",
       "8         0.000100        0.007135     0.000000e+00  \n",
       "9         0.000049        0.009583     5.551115e-17  \n",
       "10        0.000091        0.007731     5.551115e-17  \n",
       "11        0.000058        0.008902     0.000000e+00  \n",
       "12        0.000057        0.012009     0.000000e+00  \n",
       "13        0.000082        0.006056     7.850462e-17  \n",
       "14        0.000076        0.009107     0.000000e+00  \n",
       "15        0.000210        0.015396     0.000000e+00  \n",
       "16        0.000062        0.009499     0.000000e+00  \n",
       "17        0.000167        0.009202     0.000000e+00  \n",
       "18        0.000086        0.011072     0.000000e+00  \n",
       "19        0.000074        0.004088     0.000000e+00  \n",
       "20        0.000255        0.008321     0.000000e+00  \n",
       "21        0.000100        0.007739     0.000000e+00  \n",
       "22        0.000080        0.013185     5.551115e-17  \n",
       "23        0.000113        0.006803     0.000000e+00  \n",
       "24        0.000057        0.007946     2.829455e-06  \n",
       "25        0.000081        0.012351     0.000000e+00  \n",
       "26        0.003982        0.007388     0.000000e+00  \n",
       "27        0.000041        0.008370     0.000000e+00  \n",
       "28        0.000086        0.006632     0.000000e+00  \n",
       "29        0.000238        0.007469     0.000000e+00  \n",
       "30        0.000063        0.005012     0.000000e+00  \n",
       "31        0.000052        0.009494     0.000000e+00  \n",
       "32        0.000494        0.006610     0.000000e+00  \n",
       "33        0.000046        0.006903     0.000000e+00  \n",
       "34        0.000086        0.004890     0.000000e+00  \n",
       "35        0.000048        0.007695     0.000000e+00  \n",
       "36        0.000072        0.002811     0.000000e+00  \n",
       "37        0.000126        0.006677     0.000000e+00  \n",
       "38        0.000170        0.005301     0.000000e+00  \n",
       "39        0.000103        0.006026     5.551115e-17  \n",
       "40        0.000190        0.007998     0.000000e+00  \n",
       "41        0.000762        0.006041     0.000000e+00  \n",
       "42        0.000077        0.004932     0.000000e+00  \n",
       "43        0.000257        0.006039     0.000000e+00  \n",
       "44        0.000432        0.004971     0.000000e+00  \n",
       "45        0.001002        0.007065     0.000000e+00  \n",
       "46        0.000300        0.006956     0.000000e+00  \n",
       "47        0.003466        0.005601     0.000000e+00  \n",
       "48        0.000130        0.005024     0.000000e+00  \n",
       "49        0.000068        0.005290     0.000000e+00  \n",
       "50        0.000121        0.007669     0.000000e+00  \n",
       "51        0.000022        0.008065     0.000000e+00  \n",
       "52        0.000061        0.009276     0.000000e+00  \n",
       "53        0.000183        0.007283     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_DAISY_3.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: 0.930732\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       0.60      0.65      0.63       480\n",
      "          1       0.58      0.52      0.55       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": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End time:  2019-01-16 18:39:19.367414\n",
      "Uptime : 2:00:03.331741\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)"
   ]
  }
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