Day 02 - Ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diabetes Dataset\n",
    "\n",
    "1. Decision Trees\n",
    "\n",
    "2. Random Forest\n",
    "\n",
    "3. Logistic Regression\n",
    "\n",
    "4. Bagging Model\n",
    "\n",
    "5. Adaboost\n",
    "\n",
    "6. Gradient Boosting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = pd.read_csv(\".//Downloads//diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes[\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0884e0438>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Plot of Insulin and Outcome\n",
    "sns.boxplot(x = diabetes[\"Outcome\"], y = diabetes[\"Insulin\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>846</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.398</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "13            1      189             60             23      846  30.1   \n",
       "\n",
       "    DiabetesPedigreeFunction  Age  Outcome  \n",
       "13                     0.398   59        1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes[diabetes[\"Insulin\"]>800]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    164.000000\n",
       "mean      34.923171\n",
       "std        6.246684\n",
       "min       19.600000\n",
       "25%       30.775000\n",
       "50%       34.500000\n",
       "75%       38.475000\n",
       "max       57.300000\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# People who have high insulin, arey they having high BMI?\n",
    "diabetes[diabetes[\"Insulin\"]>140][\"BMI\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    386\n",
       "1    259\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes[diabetes[\"BMI\"]>25][\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    768.000000\n",
       "mean      69.105469\n",
       "std       19.355807\n",
       "min        0.000000\n",
       "25%       62.000000\n",
       "50%       72.000000\n",
       "75%       80.000000\n",
       "max      122.000000\n",
       "Name: BloodPressure, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# People who have High BP would be diabetic\n",
    "diabetes[\"BloodPressure\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    679.000000\n",
       "mean      70.287187\n",
       "std       17.783564\n",
       "min        0.000000\n",
       "25%       64.000000\n",
       "50%       72.000000\n",
       "75%       80.000000\n",
       "max      114.000000\n",
       "Name: BloodPressure, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if BMI is > 24, what is the statistical summary of BP\n",
    "diabetes[diabetes[\"BMI\"]>24][\"BloodPressure\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputting 75  as median where the BP is 0\n",
    "diabetes[\"BloodPressure\"].replace(to_replace = 0, \n",
    "                                  value = 72, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>103</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0.183</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.140</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3</td>\n",
       "      <td>113</td>\n",
       "      <td>44</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0.140</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>48</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.323</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>0.654</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "      <td>99</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.496</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>48</td>\n",
       "      <td>45</td>\n",
       "      <td>194</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.613</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>83</td>\n",
       "      <td>28.7</td>\n",
       "      <td>0.654</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2</td>\n",
       "      <td>123</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>165</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0.520</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>44</td>\n",
       "      <td>47</td>\n",
       "      <td>63</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.280</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>6</td>\n",
       "      <td>108</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>130</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.813</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.559</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>2</td>\n",
       "      <td>127</td>\n",
       "      <td>46</td>\n",
       "      <td>21</td>\n",
       "      <td>335</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0.176</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>94</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "4              0      137             40             35      168  43.1   \n",
       "18             1      103             30             38       83  43.3   \n",
       "75             1        0             48             20        0  24.7   \n",
       "80             3      113             44             13        0  22.4   \n",
       "97             1       71             48             18       76  20.4   \n",
       "117            5       78             48              0        0  33.7   \n",
       "125            1       88             30             42       99  55.0   \n",
       "293            1      128             48             45      194  40.5   \n",
       "346            1      139             46             19       83  28.7   \n",
       "405            2      123             48             32      165  42.1   \n",
       "575            1      119             44             47       63  35.5   \n",
       "576            6      108             44             20      130  24.0   \n",
       "597            1       89             24             19       25  27.8   \n",
       "599            1      109             38             18      120  23.1   \n",
       "707            2      127             46             21      335  34.4   \n",
       "741            3      102             44             20       94  30.8   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "4                       2.288   33        1  \n",
       "18                      0.183   33        0  \n",
       "75                      0.140   22        0  \n",
       "80                      0.140   22        0  \n",
       "97                      0.323   22        0  \n",
       "117                     0.654   25        0  \n",
       "125                     0.496   26        1  \n",
       "293                     0.613   24        1  \n",
       "346                     0.654   22        0  \n",
       "405                     0.520   26        0  \n",
       "575                     0.280   25        0  \n",
       "576                     0.813   35        0  \n",
       "597                     0.559   21        0  \n",
       "599                     0.407   26        0  \n",
       "707                     0.176   22        0  \n",
       "741                     0.400   26        0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes[diabetes[\"BloodPressure\"]<50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drivers of Being Diabetic\n",
    "\n",
    "1. BMI and BMI with levels greater than 25\n",
    "\n",
    "2. People with High Insulin are Diabetic\n",
    "\n",
    "3. Blood Pressure levels are impacting the Diabetic Elements in a Human\n",
    "\n",
    "4. Very Less Impact of Pregnancy seen on Being Diabetic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    768.000000\n",
       "mean      20.536458\n",
       "std       15.952218\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%       23.000000\n",
       "75%       32.000000\n",
       "max       99.000000\n",
       "Name: SkinThickness, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Skin Thickness\n",
    "diabetes.SkinThickness.describe() # Correct Range is 20-26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.575</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "579            2      197             70             99        0  34.7   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "579                     0.575   62        1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes[diabetes.SkinThickness==99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes.SkinThickness.replace(to_replace=0, value = 23, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>117.0000</td>\n",
       "      <td>140.25000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>72.386719</td>\n",
       "      <td>12.096642</td>\n",
       "      <td>24.000</td>\n",
       "      <td>64.00000</td>\n",
       "      <td>72.0000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>27.334635</td>\n",
       "      <td>9.229014</td>\n",
       "      <td>7.000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>127.25000</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.30000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>36.60000</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.3725</td>\n",
       "      <td>0.62625</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean         std     min       25%  \\\n",
       "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
       "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
       "BloodPressure             768.0   72.386719   12.096642  24.000  64.00000   \n",
       "SkinThickness             768.0   27.334635    9.229014   7.000  23.00000   \n",
       "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
       "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
       "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
       "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
       "\n",
       "                               50%        75%     max  \n",
       "Pregnancies                 3.0000    6.00000   17.00  \n",
       "Glucose                   117.0000  140.25000  199.00  \n",
       "BloodPressure              72.0000   80.00000  122.00  \n",
       "SkinThickness              23.0000   32.00000   99.00  \n",
       "Insulin                    30.5000  127.25000  846.00  \n",
       "BMI                        32.0000   36.60000   67.10  \n",
       "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
       "Age                        29.0000   41.00000   81.00  \n",
       "Outcome                     0.0000    1.00000    1.00  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    236\n",
       "1    138\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes[diabetes[\"Insulin\"]==0][\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputting the Median Value for Glucose\n",
    "diabetes[\"Glucose\"].replace(to_replace = 0, value = 117, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64             23        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9\n",
       "1    2\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute the BMI Values\n",
    "diabetes[diabetes.BMI==0][\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    500.000000\n",
       "mean      30.304200\n",
       "std        7.689855\n",
       "min        0.000000\n",
       "25%       25.400000\n",
       "50%       30.050000\n",
       "75%       35.300000\n",
       "max       57.300000\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BMI of those people where the Outcome is 0\n",
    "diabetes[diabetes.Outcome==0][\"BMI\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes[\"BMI\"].replace(to_replace = 0, value = 30, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    768.000000\n",
       "mean      32.422266\n",
       "std        6.881357\n",
       "min       18.200000\n",
       "25%       27.500000\n",
       "50%       32.000000\n",
       "75%       36.600000\n",
       "max       67.100000\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Extraction\n",
    "diabetes[\"BMI\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "Extracted a new variable called BMI CAT that has the cat values for BMI Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 18-24:: Healthy, 25-30:Overweight >30: OBESE...\n",
    "def obese_cat(x):\n",
    "    if(x<=18) and (x<=24):\n",
    "        return 0\n",
    "    elif(x>24) and (x<=30):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes[\"BMICat\"] = diabetes[\"BMI\"].apply(obese_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>BMICat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64             23        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  BMICat  \n",
       "0                     0.627   50        1       2  \n",
       "1                     0.351   31        0       1  \n",
       "2                     0.672   32        1       2  \n",
       "3                     0.167   21        0       1  \n",
       "4                     2.288   33        1       2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "BMICat                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process henceforth...\n",
    "\n",
    "1. Split the Data Into Train and Test\n",
    "\n",
    "2. Scale the Dataset\n",
    "\n",
    "3. Start Building the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the X and Ys...\n",
    "x = diabetes.drop(\"Outcome\", axis = 1)\n",
    "y = diabetes[\"Outcome\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Split Data Library\n",
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y, \n",
    "                                test_size = 0.30, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling of the Xtrain and Xtest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.fit(xtrain) # Found the Metrics:: Mean and SD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tuning the Train Dataset...\n",
    "xtrain = pd.DataFrame(sc.fit_transform(xtrain), \n",
    "                      columns=xtrain.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Used the Metrics Found to Tune the Dataset\n",
    "xtest = pd.DataFrame(sc.transform(xtest), \n",
    "                     columns=xtest.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "# Importing the Machine Learning Libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier, VotingClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistics Regression Model\n",
    "lg = LogisticRegression()\n",
    "model_lg = lg.fit(xtrain,ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7922077922077922"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the Accuracy Score\n",
    "accuracy_score(ytest, model_lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.89      0.84       143\n",
      "          1       0.78      0.64      0.70        88\n",
      "\n",
      "avg / total       0.79      0.79      0.79       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest, model_lg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision Tree Model\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "model_dtree = dtree.fit(xtrain, ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7316017316017316"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the Accuracy Score\n",
    "accuracy_score(ytest, model_dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.72      0.77       143\n",
      "          1       0.62      0.75      0.68        88\n",
      "\n",
      "avg / total       0.75      0.73      0.74       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest, model_dtree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "rf = RandomForestClassifier(criterion=\"entropy\",oob_score=True)\n",
    "model_rf = rf.fit(xtrain, ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7402597402597403"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Accuracy\n",
    "accuracy_score(ytest, model_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble of these Three Models Created\n",
    "\n",
    "In Classification problems, we use Voting Classifier to Ensemble the Models.\n",
    "\n",
    "My models have names of lg, rf and dtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:605: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:610: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions.sum(axis=1)[:, np.newaxis])\n"
     ]
    }
   ],
   "source": [
    "# Bagging Model\n",
    "bagg = BaggingClassifier(oob_score=True)\n",
    "model_bagg = bagg.fit(xtrain,ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7532467532467533"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy of Bagged Model\n",
    "accuracy_score(ytest, model_bagg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    }
   ],
   "source": [
    "# Adaboost\n",
    "ada = AdaBoostClassifier(base_estimator=rf, n_estimators=150)\n",
    "model_ada = ada.fit(xtrain,ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7619047619047619"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, model_ada)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = VotingClassifier(estimators=[(\"Decision Tree\", dtree),(\"Random Forest\", rf),\n",
    "                                  (\"Bagging\", bagg),(\"Ada Boost\", ada)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:605: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:610: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions.sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "model_vc = vc.fit(xtrain,ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7359307359307359"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest,model_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.76      0.84      0.80       143\n",
      "          1       0.69      0.58      0.63        88\n",
      "\n",
      "avg / total       0.74      0.74      0.74       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest, model_vc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gradient Boosting\n",
    "gbm = GradientBoostingClassifier()\n",
    "model_gbm = gbm.fit(xtrain, ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8008658008658008"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, model_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = VotingClassifier(estimators={(\"Bagging\", bagg), (\"GBM\", gbm)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:605: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:610: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions.sum(axis=1)[:, np.newaxis])\n",
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "model_newvc = vc.fit(xtrain, ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7705627705627706"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, model_newvc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking if XGBOOST is imported Correct\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg = xgb.XGBClassifier(n_estimators=150, learning_rate=1.0, max_depth=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\classroom2-DTP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "model_xg = xg.fit(xtrain, ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7489177489177489"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, model_xg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_params={\"eta\":[0,0.2, 0.5, 0.9, 1],\n",
    "          \"gamma\":[0,5,10,25,50],\n",
    "          \"maxdepth\":[3,4,5,6,8,10]} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "gridsrch = GridSearchCV(estimator=\"xg\", param_grid=xg_params, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'GridSearchCV' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-98-4aff3408884c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgridsrch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'GridSearchCV' object is not callable"
     ]
    }
   ],
   "source": [
    "gridsrch(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}