stop_frisk_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pickle\n",
    "pd.set_option('display.max_columns', 200)\n",
    "pd.set_option('display.max_rows', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(r'modeling-df.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "      <th>descr_SEXUAL PERFORMANCE BY A CHILD</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>140</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>180</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>160</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 232 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   perobs  perstop  explnstp  othpers  offunif  frisked  searched  contrabn  \\\n",
       "0       1        8         1        0        0        1         1         0   \n",
       "1       8       10         1        1        0        0         1         0   \n",
       "2       2       10         1        0        1        1         1         0   \n",
       "3       1        2         1        0        1        1         0         0   \n",
       "4       3       10         1        0        1        0         0         0   \n",
       "\n",
       "   pistol  riflshot  asltweap  knifcuti  machgun  othrweap  pf_hands  pf_wall  \\\n",
       "0       0         0         0         0        0         0         0        0   \n",
       "1       0         0         0         0        0         0         1        1   \n",
       "2       0         0         0         0        0         0         0        0   \n",
       "3       0         0         0         0        0         0         1        0   \n",
       "4       0         0         0         0        0         0         0        0   \n",
       "\n",
       "   pf_grnd  pf_drwep  pf_ptwep  pf_baton  pf_hcuff  pf_pepsp  pf_other  \\\n",
       "0        0         0         0         0         1         0         0   \n",
       "1        0         0         0         0         0         0         0   \n",
       "2        0         0         0         0         0         0         0   \n",
       "3        0         0         1         0         1         0         0   \n",
       "4        0         0         0         0         0         0         0   \n",
       "\n",
       "   ac_rept  ac_inves  rf_vcrim  rf_othsw  ac_proxm  rf_attir  cs_objcs  \\\n",
       "0        0         0         0         0         0         0         0   \n",
       "1        0         0         0         0         0         0         0   \n",
       "2        1         0         0         1         0         0         0   \n",
       "3        0         0         1         0         1         0         0   \n",
       "4        0         0         0         0         0         0         0   \n",
       "\n",
       "   cs_descr  cs_casng  cs_lkout  rf_vcact  cs_cloth  cs_drgtr  ac_evasv  \\\n",
       "0         0         1         0         0         0         0         0   \n",
       "1         0         0         0         0         0         1         1   \n",
       "2         1         0         0         0         0         0         0   \n",
       "3         1         0         0         0         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   ac_assoc  cs_furtv  rf_rfcmp  ac_cgdir  rf_verbl  cs_vcrim  cs_bulge  \\\n",
       "0         1         1         0         1         0         0         0   \n",
       "1         0         1         0         0         0         0         0   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         1         1         1         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   cs_other  ac_incid  ac_time  rf_knowl  ac_stsnd  ac_other  sb_hdobj  \\\n",
       "0         0         1        1         0         0         0         0   \n",
       "1         1         1        1         0         0         0         0   \n",
       "2         0         0        0         0         0         0         0   \n",
       "3         0         0        0         0         0         0         0   \n",
       "4         1         0        0         0         0         1         0   \n",
       "\n",
       "   sb_outln  sb_admis  sb_other  rf_furt  rf_bulg  age  weight  ht_inches  \\\n",
       "0         0         0         1        1        0   48     180         74   \n",
       "1         0         0         1        0        0   20     150         68   \n",
       "2         0         0         1        0        0   34     140         66   \n",
       "3         0         0         0        1        0   33     180         69   \n",
       "4         0         0         0        0        0   22     160         71   \n",
       "\n",
       "   target  pct_5  pct_6  pct_7  pct_9  pct_10  pct_13  pct_14  pct_17  pct_18  \\\n",
       "0       1      0      0      0      0       0       0       0       0       0   \n",
       "1       0      0      0      0      0       1       0       0       0       0   \n",
       "2       1      0      0      0      0       0       0       0       0       0   \n",
       "3       0      0      0      0      0       0       0       0       0       0   \n",
       "4       0      0      0      0      0       0       0       0       0       0   \n",
       "\n",
       "   pct_19  pct_20  pct_22  pct_23  pct_24  pct_25  pct_26  pct_28  pct_30  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pct_32  pct_33  pct_34  pct_40  pct_41  pct_42  pct_43  pct_44  pct_45  \\\n",
       "0       0       0       0       0       1       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pct_46  pct_47  pct_48  pct_49  pct_50  pct_52  pct_60  pct_61  pct_62  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       1       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pct_63  pct_66  pct_67  pct_68  ...  pct_120  pct_121  pct_122  pct_123  \\\n",
       "0       0       0       0       0  ...        0        0        0        0   \n",
       "1       0       0       0       0  ...        0        0        0        0   \n",
       "2       0       1       0       0  ...        0        0        0        0   \n",
       "3       0       0       0       0  ...        0        0        0        0   \n",
       "4       0       0       0       0  ...        0        0        0        0   \n",
       "\n",
       "   inout_Outside  trhsloc_Neither  trhsloc_Transit  typeofid_P  typeofid_R  \\\n",
       "0              1                1                0           1           0   \n",
       "1              1                1                0           1           0   \n",
       "2              0                1                0           1           0   \n",
       "3              1                0                0           0           0   \n",
       "4              1                1                0           1           0   \n",
       "\n",
       "   typeofid_V  sex_Male  sex_Other  race_ASIAN/PACIFIC ISLANDER  race_BLACK  \\\n",
       "0           0         1          0                            0           1   \n",
       "1           0         1          0                            0           0   \n",
       "2           0         1          0                            0           0   \n",
       "3           1         1          0                            0           1   \n",
       "4           0         1          0                            0           1   \n",
       "\n",
       "   race_BLACK-HISPANIC  race_OTHER  race_UNKNOWN  race_WHITE  \\\n",
       "0                    0           0             0           0   \n",
       "1                    0           0             0           1   \n",
       "2                    1           0             0           0   \n",
       "3                    0           0             0           0   \n",
       "4                    0           0             0           0   \n",
       "\n",
       "   race_WHITE-HISPANIC  build_M  build_T  build_U  build_Z  city_BROOKLYN  \\\n",
       "0                    0        0        1        0        0              0   \n",
       "1                    0        0        1        0        0              0   \n",
       "2                    0        1        0        0        0              1   \n",
       "3                    0        1        0        0        0              0   \n",
       "4                    0        0        1        0        0              1   \n",
       "\n",
       "   city_MANHATTAN  city_QUEENS  city_STATEN IS  month_August  month_December  \\\n",
       "0               0            0               0             0               0   \n",
       "1               1            0               0             0               0   \n",
       "2               0            0               0             0               0   \n",
       "3               0            0               0             0               0   \n",
       "4               0            0               0             0               0   \n",
       "\n",
       "   month_February  month_January  month_July  month_June  month_March  \\\n",
       "0               1              0           0           0            0   \n",
       "1               1              0           0           0            0   \n",
       "2               0              1           0           0            0   \n",
       "3               0              1           0           0            0   \n",
       "4               0              1           0           0            0   \n",
       "\n",
       "   month_May  month_November  month_October  month_September  \\\n",
       "0          0               0              0                0   \n",
       "1          0               0              0                0   \n",
       "2          0               0              0                0   \n",
       "3          0               0              0                0   \n",
       "4          0               0              0                0   \n",
       "\n",
       "   descr_AGGRAVATED HARASSMENT  descr_ARSON  descr_ASSAULT  \\\n",
       "0                            0            0              0   \n",
       "1                            0            0              0   \n",
       "2                            0            0              1   \n",
       "3                            0            0              0   \n",
       "4                            0            0              0   \n",
       "\n",
       "   descr_AUTO STRIPPING  descr_BIGAMY  descr_BRIBE RECEIVING  descr_BURGLARY  \\\n",
       "0                     0             0                      0               1   \n",
       "1                     0             0                      0               0   \n",
       "2                     0             0                      0               0   \n",
       "3                     0             0                      0               0   \n",
       "4                     0             0                      0               0   \n",
       "\n",
       "   descr_COERCION  descr_COMPUTER TRESPASS  descr_COURSE OF SEXUAL CONDUCT  \\\n",
       "0               0                        0                               0   \n",
       "1               0                        0                               0   \n",
       "2               0                        0                               0   \n",
       "3               0                        0                               0   \n",
       "4               0                        0                               0   \n",
       "\n",
       "   descr_CPSP  descr_CPW  descr_CREATING A HAZARD  descr_CRIMINAL CONTEMPT  \\\n",
       "0           0          0                        0                        0   \n",
       "1           0          0                        0                        0   \n",
       "2           0          0                        0                        0   \n",
       "3           0          1                        0                        0   \n",
       "4           0          0                        0                        0   \n",
       "\n",
       "   descr_CRIMINAL MISCHIEF  descr_CRIMINAL POSSESION OF CONTROLLED SUBSTANCE  \\\n",
       "0                        0                                                 0   \n",
       "1                        0                                                 0   \n",
       "2                        0                                                 0   \n",
       "3                        0                                                 0   \n",
       "4                        0                                                 0   \n",
       "\n",
       "   descr_CRIMINAL POSSESSION OF COMPUTER MATERIAL  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   descr_CRIMINAL POSSESSION OF FORGED INSTRUMENT  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   descr_CRIMINAL POSSESSION OF MARIHUANA  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "\n",
       "   descr_CRIMINAL SALE OF CONTROLLED SUBSTANCE  \\\n",
       "0                                            0   \n",
       "1                                            1   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "\n",
       "   descr_CRIMINAL SALE OF MARIHUANA  descr_CRIMINAL TAMPERING  \\\n",
       "0                                 0                         0   \n",
       "1                                 0                         0   \n",
       "2                                 0                         0   \n",
       "3                                 0                         0   \n",
       "4                                 0                         0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS  descr_ENDANGER THE WELFARE OF A CHILD  \\\n",
       "0                        0                                      0   \n",
       "1                        0                                      0   \n",
       "2                        0                                      0   \n",
       "3                        0                                      0   \n",
       "4                        0                                      0   \n",
       "\n",
       "   descr_ESCAPE  descr_FORCIBLE TOUCHING  descr_FORGERY  descr_FRAUD  \\\n",
       "0             0                        0              0            0   \n",
       "1             0                        0              0            0   \n",
       "2             0                        0              0            0   \n",
       "3             0                        0              0            0   \n",
       "4             0                        0              0            0   \n",
       "\n",
       "   descr_FRAUDULENT ACCOSTING  descr_GAMBLING  descr_GRAND LARCENY  \\\n",
       "0                           0               0                    0   \n",
       "1                           0               0                    0   \n",
       "2                           0               0                    0   \n",
       "3                           0               0                    0   \n",
       "4                           0               0                    0   \n",
       "\n",
       "   descr_GRAND LARCENY AUTO  descr_HARASSMENT  descr_JOSTLING  \\\n",
       "0                         0                 0               0   \n",
       "1                         0                 0               0   \n",
       "2                         0                 0               0   \n",
       "3                         0                 0               0   \n",
       "4                         0                 0               0   \n",
       "\n",
       "   descr_KIDNAPPING  descr_LOITERING  descr_MAKING GRAFFITI  descr_MENACING  \\\n",
       "0                 0                0                      0               0   \n",
       "1                 0                0                      0               0   \n",
       "2                 0                0                      0               0   \n",
       "3                 0                0                      0               0   \n",
       "4                 0                0                      0               0   \n",
       "\n",
       "   descr_MISAPPLICATION OF PROPERTY  descr_MURDER  \\\n",
       "0                                 0             0   \n",
       "1                                 0             0   \n",
       "2                                 0             0   \n",
       "3                                 0             0   \n",
       "4                                 0             0   \n",
       "\n",
       "   descr_OBSTRUCTING GOVERNMENTAL ADMINISTRATION  descr_OTHER  \\\n",
       "0                                              0            0   \n",
       "1                                              0            0   \n",
       "2                                              0            0   \n",
       "3                                              0            0   \n",
       "4                                              0            1   \n",
       "\n",
       "   descr_PETIT LARCENY  descr_POSSESSION OF BURGLAR TOOLS  \\\n",
       "0                    0                                  0   \n",
       "1                    0                                  0   \n",
       "2                    0                                  0   \n",
       "3                    0                                  0   \n",
       "4                    0                                  0   \n",
       "\n",
       "   descr_POSSESSION OF GRAFFITI INSTRUMENTS  descr_PROHIBITED USE OF WEAPON  \\\n",
       "0                                         0                               0   \n",
       "1                                         0                               0   \n",
       "2                                         0                               0   \n",
       "3                                         0                               0   \n",
       "4                                         0                               0   \n",
       "\n",
       "   descr_PROSTITUTION  descr_PUBLIC LEWDNESS  descr_RAPE  \\\n",
       "0                   0                      0           0   \n",
       "1                   0                      0           0   \n",
       "2                   0                      0           0   \n",
       "3                   0                      0           0   \n",
       "4                   0                      0           0   \n",
       "\n",
       "   descr_RECKLESS ENDANGERMENT  \\\n",
       "0                            0   \n",
       "1                            0   \n",
       "2                            0   \n",
       "3                            0   \n",
       "4                            0   \n",
       "\n",
       "   descr_REFUSING TO AID A PEACE OR POLICE OFFICER  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "\n",
       "   descr_REWARD OFFICIAL MISCONDUCT  descr_ROBBERY  descr_SEXUAL ABUSE  \\\n",
       "0                                 0              0                   0   \n",
       "1                                 0              0                   0   \n",
       "2                                 0              0                   0   \n",
       "3                                 0              0                   0   \n",
       "4                                 0              0                   0   \n",
       "\n",
       "   descr_SEXUAL MISCONDUCT  descr_SEXUAL PERFORMANCE BY A CHILD  \\\n",
       "0                        0                                    0   \n",
       "1                        0                                    0   \n",
       "2                        0                                    0   \n",
       "3                        0                                    0   \n",
       "4                        0                                    0   \n",
       "\n",
       "   descr_TERRORISM  descr_THEFT OF SERVICES  descr_UNAUTHORIZED RECORDING  \\\n",
       "0                0                        0                             0   \n",
       "1                0                        0                             0   \n",
       "2                0                        0                             0   \n",
       "3                0                        0                             0   \n",
       "4                0                        0                             0   \n",
       "\n",
       "   descr_UNAUTHORIZED USE OF A VEHICLE  descr_UNLAWFUL ASSEMBLY  \\\n",
       "0                                    0                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    0                        0   \n",
       "4                                    0                        0   \n",
       "\n",
       "   descr_UNLAWFULL IMPRISONMENT  \n",
       "0                             0  \n",
       "1                             0  \n",
       "2                             0  \n",
       "3                             0  \n",
       "4                             0  \n",
       "\n",
       "[5 rows x 232 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the dataset for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the df into target df and feature df\n",
    "df_target = df.target\n",
    "df_features = df.drop(['target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = df_features.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating our train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_features, df_target, random_state=1, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize our data using min-max scaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Instatiate the scaler\n",
    "scaler = MinMaxScaler()  \n",
    "\n",
    "# Fit the scaler to our training feature set\n",
    "scaler.fit(X_train)\n",
    "\n",
    "# Using the fitted scaler, transform the training feature set to values between 0 and 1\n",
    "X_train = scaler.transform(X_train)  \n",
    "\n",
    "# Transform the testing set as well to ensure consistency in predictions across feature sets\n",
    "X_test = scaler.transform(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list to house performance metrics\n",
    "performance = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting our basic logistic regression model.\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "lr = LogisticRegression(class_weight='balanced', max_iter=1000)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "lr_basic_train = lr.predict(X_train)\n",
    "\n",
    "lr_basic_preds = lr.predict(X_test)\n",
    "\n",
    "# Train Metrics\n",
    "lr_basic_train_accuracy = metrics.accuracy_score(y_train, lr_basic_train)\n",
    "lr_basic_train_precision = metrics.precision_score(y_train, lr_basic_train)\n",
    "lr_basic_train_recall = metrics.recall_score(y_train, lr_basic_train)\n",
    "lr_basic_train_f1 = metrics.f1_score(y_train, lr_basic_train)\n",
    "\n",
    "\n",
    "# Test Metrics\n",
    "lr_basic_accuracy = metrics.accuracy_score(y_test, lr_basic_preds)\n",
    "lr_basic_precision = metrics.precision_score(y_test, lr_basic_preds)\n",
    "lr_basic_recall = metrics.recall_score(y_test, lr_basic_preds)\n",
    "lr_basic_f1 = metrics.f1_score(y_test, lr_basic_preds)\n",
    "\n",
    "\n",
    "\n",
    "performance.append({'model': 'Basic Logistic Regression', '# Features': df_features.shape[1], 'Train Accuracy': lr_basic_train_accuracy,\n",
    "                    'Test Accuracy': lr_basic_accuracy, 'Train Recall': lr_basic_train_recall,\n",
    "                    'Test Recall': lr_basic_recall, 'Train Precision': lr_basic_train_precision,\n",
    "                    'Test Precision': lr_basic_precision, 'Train F1': lr_basic_train_f1,\n",
    "                    'Test F1': lr_basic_f1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.73308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model  # Features  Train Accuracy  Test Accuracy  \\\n",
       "0  Basic Logistic Regression         231        0.861635       0.858525   \n",
       "\n",
       "   Train Recall  Test Recall  Train Precision  Test Precision  Train F1  \\\n",
       "0      0.801843     0.796694         0.680171        0.678873  0.736012   \n",
       "\n",
       "   Test F1  \n",
       "0  0.73308  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Now, using a standard scaler instead of Min-Max (although, Min-Max is recommended for classification problems)\n",
    "# # Instatiate the scaler\n",
    "# scaler = StandardScaler()  \n",
    "\n",
    "# # Fit the scaler to our training feature set\n",
    "# scaler.fit(X_train)\n",
    "\n",
    "# # Using the fitted scaler, transform the training feature set to values between 0 and 1\n",
    "# X_train = scaler.transform(X_train)  \n",
    "\n",
    "# # Transform the testing set as well to ensure consistency in predictions across feature sets\n",
    "# X_test = scaler.transform(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Fitting our basic logistic regression model.\n",
    "\n",
    "# lr = LogisticRegression(class_weight='balanced', max_iter=1000)\n",
    "# lr.fit(X_train, y_train)\n",
    "\n",
    "# lr_basic_preds = lr.predict(X_test)\n",
    "\n",
    "# lr_basic_f1 = metrics.f1_score(y_test, lr_basic_preds)\n",
    "\n",
    "# print(lr_basic_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Min-Max scaler performed better, so going to use that moving forward\n",
    "# # Normalize our data using min-max scaler\n",
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# # Instatiate the scaler\n",
    "# scaler = MinMaxScaler()  \n",
    "\n",
    "# # Fit the scaler to our training feature set\n",
    "# scaler.fit(X_train)\n",
    "\n",
    "# # Using the fitted scaler, transform the training feature set to values between 0 and 1\n",
    "# X_train = scaler.transform(X_train)  \n",
    "\n",
    "# # Transform the testing set as well to ensure consistency in predictions across feature sets\n",
    "# X_test = scaler.transform(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 50 folds for each of 10 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    2.8s\n",
      "[Parallel(n_jobs=-1)]: Done 260 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=50, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10],\n",
       "                         'class_weight': ['balanced'], 'max_iter': [1000],\n",
       "                         'penalty': ['l1', 'l2']},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying gridsearchCV to model\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = { \n",
    "     \"class_weight\":['balanced'],\n",
    "     \"penalty\": ['l1', 'l2'],\n",
    "     \"C\":[0.001,0.01,0.1,1,10],\n",
    "     \"max_iter\": [1000]\n",
    "}\n",
    "\n",
    "grid_log = GridSearchCV(LogisticRegression(), param_grid, cv=50, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "grid_log.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)\n",
      "{'C': 0.1, 'class_weight': 'balanced', 'max_iter': 1000, 'penalty': 'l2'}\n",
      "0.7226465527112268\n"
     ]
    }
   ],
   "source": [
    "print(grid_log.best_estimator_)\n",
    "print(grid_log.best_params_)\n",
    "print(grid_log.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions for training set\n",
    "log_grid_preds_train = grid_log.best_estimator_.predict(X_train)\n",
    "\n",
    "# Predictions for testing set\n",
    "log_grid_preds = grid_log.best_estimator_.predict(X_test)\n",
    "\n",
    "# Training set metrics\n",
    "log_grid_train_accuracy = metrics.accuracy_score(y_train, log_grid_preds_train)\n",
    "log_grid_train_precision = metrics.precision_score(y_train, log_grid_preds_train)\n",
    "log_grid_train_recall = metrics.recall_score(y_train, log_grid_preds_train)\n",
    "log_grid_train_f1 = metrics.f1_score(y_train, log_grid_preds_train)\n",
    "\n",
    "# Testing set metrics\n",
    "log_grid_accuracy = metrics.accuracy_score(y_test, log_grid_preds)\n",
    "log_grid_precision = metrics.precision_score(y_test, log_grid_preds)\n",
    "log_grid_recall = metrics.recall_score(y_test, log_grid_preds)\n",
    "log_grid_f1 = metrics.f1_score(y_test, log_grid_preds)\n",
    "\n",
    "performance.append({'model': 'GridSearchCV Logistic Regression', '# Features': df_features.shape[1],'Train Accuracy': log_grid_train_accuracy,\n",
    "                    'Test Accuracy': log_grid_accuracy, 'Train Recall': log_grid_train_recall, \n",
    "                    'Test Recall': lr_basic_recall, 'Train Precision': log_grid_train_precision,\n",
    "                    'Test Precision': log_grid_precision, 'Train F1': log_grid_train_f1,\n",
    "                    'Test F1': log_grid_f1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              model  # Features  Train Accuracy  \\\n",
       "0         Basic Logistic Regression         231        0.861635   \n",
       "1  GridSearchCV Logistic Regression         231        0.860425   \n",
       "\n",
       "   Test Accuracy  Train Recall  Test Recall  Train Precision  Test Precision  \\\n",
       "0       0.858525      0.801843     0.796694         0.680171        0.678873   \n",
       "1       0.855300      0.788018     0.796694         0.681522        0.674221   \n",
       "\n",
       "   Train F1   Test F1  \n",
       "0  0.736012  0.733080  \n",
       "1  0.730911  0.726163  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "### Significant Features Only\n",
    "Before creating any additional models, let's see how our models perform with a feature set containing only significant features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_keep = pd.read_pickle(r'significant_features_df.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features_selected = df_features.loc[:,list(df_keep.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conduct Train Test split on new feature set\n",
    "X_train_sel, X_test_sel, y_train_sel, y_test_sel = train_test_split(df_features_selected, df_target, \n",
    "                                                                    random_state=1, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize our data using min-max scaler\n",
    "\n",
    "# Fit the scaler to our training feature set\n",
    "scaler.fit(X_train_sel)\n",
    "\n",
    "# Using the fitted scaler, transform the training feature set to values between 0 and 1\n",
    "X_train_sel = scaler.transform(X_train_sel)  \n",
    "\n",
    "# Transform the testing set as well to ensure consistency in predictions across feature sets\n",
    "X_test_sel = scaler.transform(X_test_sel) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting our basic logistic regression model.\n",
    "\n",
    "lr = LogisticRegression(class_weight='balanced', max_iter=1000)\n",
    "lr.fit(X_train_sel, y_train_sel)\n",
    "\n",
    "lr_basic_train_sel = lr.predict(X_train_sel)\n",
    "\n",
    "lr_basic_preds_sel = lr.predict(X_test_sel)\n",
    "\n",
    "# Train Metrics\n",
    "lr_basic_train_accuracy_sel = metrics.accuracy_score(y_train_sel, lr_basic_train_sel)\n",
    "lr_basic_train_precision_sel = metrics.precision_score(y_train_sel, lr_basic_train_sel)\n",
    "lr_basic_train_recall_sel = metrics.recall_score(y_train_sel, lr_basic_train_sel)\n",
    "lr_basic_train_f1_sel = metrics.f1_score(y_train_sel, lr_basic_train_sel)\n",
    "\n",
    "\n",
    "# Test Metrics\n",
    "lr_basic_accuracy_sel = metrics.accuracy_score(y_test_sel, lr_basic_preds_sel)\n",
    "lr_basic_precision_sel = metrics.precision_score(y_test_sel, lr_basic_preds_sel)\n",
    "lr_basic_recall_sel = metrics.recall_score(y_test_sel, lr_basic_preds_sel)\n",
    "lr_basic_f1_sel = metrics.f1_score(y_test_sel, lr_basic_preds_sel)\n",
    "\n",
    "\n",
    "performance.append({'model': 'Basic Logistic Regression Selected', '# Features': df_features_selected.shape[1], 'Train Accuracy': lr_basic_train_accuracy_sel,\n",
    "                    'Test Accuracy': lr_basic_accuracy_sel, 'Train Recall': lr_basic_train_recall_sel,\n",
    "                    'Test Recall': lr_basic_recall_sel, 'Train Precision': lr_basic_train_precision_sel,\n",
    "                    'Test Precision': lr_basic_precision_sel, 'Train F1': lr_basic_train_f1_sel,\n",
    "                    'Test F1': lr_basic_f1_sel})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                model  # Features  Train Accuracy  \\\n",
       "0           Basic Logistic Regression         231        0.861635   \n",
       "1    GridSearchCV Logistic Regression         231        0.860425   \n",
       "2  Basic Logistic Regression Selected          99        0.859216   \n",
       "\n",
       "   Test Accuracy  Train Recall  Test Recall  Train Precision  Test Precision  \\\n",
       "0       0.858525      0.801843     0.796694         0.680171        0.678873   \n",
       "1       0.855300      0.788018     0.796694         0.681522        0.674221   \n",
       "2       0.858928      0.795140     0.793388         0.676408        0.680851   \n",
       "\n",
       "   Train F1   Test F1  \n",
       "0  0.736012  0.733080  \n",
       "1  0.730911  0.726163  \n",
       "2  0.730984  0.732824  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model performed about as well as the previous models with fewer features. As such, I'll move forward with\n",
    "# the feature set that has fewer features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>test_stat</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pct_20</th>\n",
       "      <td>0.003612</td>\n",
       "      <td>0.001337</td>\n",
       "      <td>1.961900</td>\n",
       "      <td>0.049800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_109</th>\n",
       "      <td>0.021568</td>\n",
       "      <td>0.015709</td>\n",
       "      <td>1.986800</td>\n",
       "      <td>0.046900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_CRIMINAL POSSESSION OF FORGED INSTRUMENT</th>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>-2.035400</td>\n",
       "      <td>0.041800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_94</th>\n",
       "      <td>0.006694</td>\n",
       "      <td>0.003342</td>\n",
       "      <td>2.087700</td>\n",
       "      <td>0.036800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_33</th>\n",
       "      <td>0.004462</td>\n",
       "      <td>0.007687</td>\n",
       "      <td>-2.128200</td>\n",
       "      <td>0.033300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_OTHER</th>\n",
       "      <td>0.012431</td>\n",
       "      <td>0.007687</td>\n",
       "      <td>2.139700</td>\n",
       "      <td>0.032400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_9</th>\n",
       "      <td>0.007544</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>-2.150500</td>\n",
       "      <td>0.031500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_MANHATTAN</th>\n",
       "      <td>0.198258</td>\n",
       "      <td>0.217246</td>\n",
       "      <td>-2.250000</td>\n",
       "      <td>0.024400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_October</th>\n",
       "      <td>0.067042</td>\n",
       "      <td>0.079880</td>\n",
       "      <td>-2.395200</td>\n",
       "      <td>0.016600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_Other</th>\n",
       "      <td>0.005525</td>\n",
       "      <td>0.002005</td>\n",
       "      <td>2.458100</td>\n",
       "      <td>0.014000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_cloth</th>\n",
       "      <td>0.034637</td>\n",
       "      <td>0.025401</td>\n",
       "      <td>2.485000</td>\n",
       "      <td>0.013000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_52</th>\n",
       "      <td>0.012643</td>\n",
       "      <td>0.007019</td>\n",
       "      <td>2.537000</td>\n",
       "      <td>0.011200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pf_ptwep</th>\n",
       "      <td>0.010306</td>\n",
       "      <td>0.016043</td>\n",
       "      <td>-2.543100</td>\n",
       "      <td>0.011000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_lkout</th>\n",
       "      <td>0.073842</td>\n",
       "      <td>0.060160</td>\n",
       "      <td>2.545900</td>\n",
       "      <td>0.010900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ac_rept</th>\n",
       "      <td>0.296749</td>\n",
       "      <td>0.272393</td>\n",
       "      <td>2.555200</td>\n",
       "      <td>0.010600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typeofid_V</th>\n",
       "      <td>0.364747</td>\n",
       "      <td>0.338904</td>\n",
       "      <td>2.567700</td>\n",
       "      <td>0.010200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_Male</th>\n",
       "      <td>0.926583</td>\n",
       "      <td>0.912099</td>\n",
       "      <td>2.590100</td>\n",
       "      <td>0.009600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explnstp</th>\n",
       "      <td>0.997663</td>\n",
       "      <td>0.994652</td>\n",
       "      <td>2.595300</td>\n",
       "      <td>0.009500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_32</th>\n",
       "      <td>0.015618</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>-2.595400</td>\n",
       "      <td>0.009400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_PETIT LARCENY</th>\n",
       "      <td>0.045155</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>-2.600500</td>\n",
       "      <td>0.009300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_104</th>\n",
       "      <td>0.004250</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>2.632400</td>\n",
       "      <td>0.008500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_84</th>\n",
       "      <td>0.018593</td>\n",
       "      <td>0.011364</td>\n",
       "      <td>2.676400</td>\n",
       "      <td>0.007400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_44</th>\n",
       "      <td>0.011475</td>\n",
       "      <td>0.005682</td>\n",
       "      <td>2.763500</td>\n",
       "      <td>0.005700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_114</th>\n",
       "      <td>0.033255</td>\n",
       "      <td>0.023061</td>\n",
       "      <td>2.811400</td>\n",
       "      <td>0.004900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_RECKLESS ENDANGERMENT</th>\n",
       "      <td>0.004675</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>2.847900</td>\n",
       "      <td>0.004400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_BLACK</th>\n",
       "      <td>0.531130</td>\n",
       "      <td>0.501003</td>\n",
       "      <td>2.874300</td>\n",
       "      <td>0.004000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_vcrim</th>\n",
       "      <td>0.259669</td>\n",
       "      <td>0.232620</td>\n",
       "      <td>2.964000</td>\n",
       "      <td>0.003000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_102</th>\n",
       "      <td>0.009775</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>3.012000</td>\n",
       "      <td>0.002600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_50</th>\n",
       "      <td>0.009987</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>3.093700</td>\n",
       "      <td>0.002000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_GRAND LARCENY</th>\n",
       "      <td>0.053230</td>\n",
       "      <td>0.039104</td>\n",
       "      <td>3.093400</td>\n",
       "      <td>0.002000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_79</th>\n",
       "      <td>0.021462</td>\n",
       "      <td>0.012032</td>\n",
       "      <td>3.275300</td>\n",
       "      <td>0.001100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_123</th>\n",
       "      <td>0.005844</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>-3.286700</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_19</th>\n",
       "      <td>0.007119</td>\n",
       "      <td>0.013703</td>\n",
       "      <td>-3.377100</td>\n",
       "      <td>0.000700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_61</th>\n",
       "      <td>0.005631</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>-3.444200</td>\n",
       "      <td>0.000600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_22</th>\n",
       "      <td>0.004675</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>3.440000</td>\n",
       "      <td>0.000600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_115</th>\n",
       "      <td>0.009137</td>\n",
       "      <td>0.016711</td>\n",
       "      <td>-3.465500</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_furt</th>\n",
       "      <td>0.179027</td>\n",
       "      <td>0.208222</td>\n",
       "      <td>-3.574500</td>\n",
       "      <td>0.000400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_rfcmp</th>\n",
       "      <td>0.070229</td>\n",
       "      <td>0.090241</td>\n",
       "      <td>-3.618800</td>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_13</th>\n",
       "      <td>0.007012</td>\n",
       "      <td>0.014037</td>\n",
       "      <td>-3.603000</td>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ac_proxm</th>\n",
       "      <td>0.364216</td>\n",
       "      <td>0.326872</td>\n",
       "      <td>3.718000</td>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_63</th>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.001671</td>\n",
       "      <td>3.748400</td>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_105</th>\n",
       "      <td>0.015512</td>\n",
       "      <td>0.025735</td>\n",
       "      <td>-3.666000</td>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_bulg</th>\n",
       "      <td>0.075967</td>\n",
       "      <td>0.098262</td>\n",
       "      <td>-3.886100</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ac_cgdir</th>\n",
       "      <td>0.128772</td>\n",
       "      <td>0.157086</td>\n",
       "      <td>-3.940500</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_72</th>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.016377</td>\n",
       "      <td>-4.027300</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trhsloc_Transit</th>\n",
       "      <td>0.038355</td>\n",
       "      <td>0.054479</td>\n",
       "      <td>-3.819300</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_UNAUTHORIZED USE OF A VEHICLE</th>\n",
       "      <td>0.001806</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>-4.720700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_47</th>\n",
       "      <td>0.013600</td>\n",
       "      <td>0.024398</td>\n",
       "      <td>-4.075200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_40</th>\n",
       "      <td>0.020506</td>\n",
       "      <td>0.060829</td>\n",
       "      <td>-11.220900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_ROBBERY</th>\n",
       "      <td>0.164046</td>\n",
       "      <td>0.124666</td>\n",
       "      <td>5.190900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_60</th>\n",
       "      <td>0.013706</td>\n",
       "      <td>0.049799</td>\n",
       "      <td>-11.618500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_THEFT OF SERVICES</th>\n",
       "      <td>0.005950</td>\n",
       "      <td>0.021390</td>\n",
       "      <td>-7.516300</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_CRIMINAL TRESPASS</th>\n",
       "      <td>0.028581</td>\n",
       "      <td>0.185829</td>\n",
       "      <td>-30.069500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contrabn</th>\n",
       "      <td>0.005631</td>\n",
       "      <td>0.228275</td>\n",
       "      <td>-44.903200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_49</th>\n",
       "      <td>0.050255</td>\n",
       "      <td>0.031083</td>\n",
       "      <td>4.377500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pf_hcuff</th>\n",
       "      <td>0.105397</td>\n",
       "      <td>0.333556</td>\n",
       "      <td>-29.621300</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pistol</th>\n",
       "      <td>0.001594</td>\n",
       "      <td>0.051136</td>\n",
       "      <td>-20.422400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_CRIMINAL POSSESSION OF MARIHUANA</th>\n",
       "      <td>0.021037</td>\n",
       "      <td>0.052139</td>\n",
       "      <td>-8.900100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frisked</th>\n",
       "      <td>0.611772</td>\n",
       "      <td>0.728944</td>\n",
       "      <td>-11.631400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_GRAND LARCENY AUTO</th>\n",
       "      <td>0.064917</td>\n",
       "      <td>0.036765</td>\n",
       "      <td>5.732900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pf_grnd</th>\n",
       "      <td>0.008925</td>\n",
       "      <td>0.029746</td>\n",
       "      <td>-8.459700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knifcuti</th>\n",
       "      <td>0.007437</td>\n",
       "      <td>0.104612</td>\n",
       "      <td>-26.766200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_43</th>\n",
       "      <td>0.019550</td>\n",
       "      <td>0.039773</td>\n",
       "      <td>-6.242000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_42</th>\n",
       "      <td>0.010943</td>\n",
       "      <td>0.027406</td>\n",
       "      <td>-6.471500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>searched</th>\n",
       "      <td>0.106354</td>\n",
       "      <td>0.654078</td>\n",
       "      <td>-61.240900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>othrweap</th>\n",
       "      <td>0.005631</td>\n",
       "      <td>0.043449</td>\n",
       "      <td>-14.945900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pf_hands</th>\n",
       "      <td>0.103379</td>\n",
       "      <td>0.143382</td>\n",
       "      <td>-6.019900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sb_outln</th>\n",
       "      <td>0.008287</td>\n",
       "      <td>0.048128</td>\n",
       "      <td>-14.318500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_CRIMINAL POSSESION OF CONTROLLED SUBSTANCE</th>\n",
       "      <td>0.011687</td>\n",
       "      <td>0.024398</td>\n",
       "      <td>-5.023500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>othpers</th>\n",
       "      <td>0.347535</td>\n",
       "      <td>0.247660</td>\n",
       "      <td>10.172900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inout_Outside</th>\n",
       "      <td>0.846260</td>\n",
       "      <td>0.673797</td>\n",
       "      <td>20.727000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trhsloc_Neither</th>\n",
       "      <td>0.877072</td>\n",
       "      <td>0.688837</td>\n",
       "      <td>23.970800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typeofid_P</th>\n",
       "      <td>0.558542</td>\n",
       "      <td>0.620321</td>\n",
       "      <td>-5.951800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typeofid_R</th>\n",
       "      <td>0.055036</td>\n",
       "      <td>0.007019</td>\n",
       "      <td>11.222100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_110</th>\n",
       "      <td>0.006269</td>\n",
       "      <td>0.020722</td>\n",
       "      <td>-7.006900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_ASIAN/PACIFIC ISLANDER</th>\n",
       "      <td>0.064598</td>\n",
       "      <td>0.043115</td>\n",
       "      <td>4.330000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sb_admis</th>\n",
       "      <td>0.003612</td>\n",
       "      <td>0.054144</td>\n",
       "      <td>-19.307200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_106</th>\n",
       "      <td>0.118785</td>\n",
       "      <td>0.048797</td>\n",
       "      <td>11.023200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_bulge</th>\n",
       "      <td>0.066086</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>-4.573600</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_WHITE-HISPANIC</th>\n",
       "      <td>0.208989</td>\n",
       "      <td>0.262701</td>\n",
       "      <td>-6.158600</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sb_other</th>\n",
       "      <td>0.058330</td>\n",
       "      <td>0.496992</td>\n",
       "      <td>-56.428200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_QUEENS</th>\n",
       "      <td>0.275181</td>\n",
       "      <td>0.209225</td>\n",
       "      <td>7.171100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_furtv</th>\n",
       "      <td>0.171908</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>-4.212600</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_othsw</th>\n",
       "      <td>0.200595</td>\n",
       "      <td>0.297794</td>\n",
       "      <td>-11.107600</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ac_evasv</th>\n",
       "      <td>0.108054</td>\n",
       "      <td>0.148730</td>\n",
       "      <td>-6.010700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_drgtr</th>\n",
       "      <td>0.038037</td>\n",
       "      <td>0.092914</td>\n",
       "      <td>-11.855500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_76</th>\n",
       "      <td>0.007862</td>\n",
       "      <td>0.019051</td>\n",
       "      <td>-5.215100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_75</th>\n",
       "      <td>0.031024</td>\n",
       "      <td>0.012701</td>\n",
       "      <td>5.425400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_67</th>\n",
       "      <td>0.026243</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>6.179300</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_casng</th>\n",
       "      <td>0.146303</td>\n",
       "      <td>0.107286</td>\n",
       "      <td>5.408400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_ASSAULT</th>\n",
       "      <td>0.101041</td>\n",
       "      <td>0.071524</td>\n",
       "      <td>4.821100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sb_hdobj</th>\n",
       "      <td>0.043455</td>\n",
       "      <td>0.101604</td>\n",
       "      <td>-11.903500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_BURGLARY</th>\n",
       "      <td>0.105716</td>\n",
       "      <td>0.057821</td>\n",
       "      <td>7.813900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_COMPUTER TRESPASS</th>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>-5.749600</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_objcs</th>\n",
       "      <td>0.023799</td>\n",
       "      <td>0.047126</td>\n",
       "      <td>-6.576700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>descr_CPW</th>\n",
       "      <td>0.300786</td>\n",
       "      <td>0.236965</td>\n",
       "      <td>6.733600</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs_descr</th>\n",
       "      <td>0.495113</td>\n",
       "      <td>0.404412</td>\n",
       "      <td>8.655800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>28.097535</td>\n",
       "      <td>28.726939</td>\n",
       "      <td>-2.554680</td>\n",
       "      <td>0.010658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>169.810136</td>\n",
       "      <td>168.001337</td>\n",
       "      <td>2.675898</td>\n",
       "      <td>0.007477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           0           1  \\\n",
       "pct_20                                              0.003612    0.001337   \n",
       "pct_109                                             0.021568    0.015709   \n",
       "descr_CRIMINAL POSSESSION OF FORGED INSTRUMENT      0.001912    0.004011   \n",
       "pct_94                                              0.006694    0.003342   \n",
       "pct_33                                              0.004462    0.007687   \n",
       "race_OTHER                                          0.012431    0.007687   \n",
       "pct_9                                               0.007544    0.011698   \n",
       "city_MANHATTAN                                      0.198258    0.217246   \n",
       "month_October                                       0.067042    0.079880   \n",
       "sex_Other                                           0.005525    0.002005   \n",
       "cs_cloth                                            0.034637    0.025401   \n",
       "pct_52                                              0.012643    0.007019   \n",
       "pf_ptwep                                            0.010306    0.016043   \n",
       "cs_lkout                                            0.073842    0.060160   \n",
       "ac_rept                                             0.296749    0.272393   \n",
       "typeofid_V                                          0.364747    0.338904   \n",
       "sex_Male                                            0.926583    0.912099   \n",
       "explnstp                                            0.997663    0.994652   \n",
       "pct_32                                              0.015618    0.022727   \n",
       "descr_PETIT LARCENY                                 0.045155    0.056818   \n",
       "pct_104                                             0.004250    0.001003   \n",
       "pct_84                                              0.018593    0.011364   \n",
       "pct_44                                              0.011475    0.005682   \n",
       "pct_114                                             0.033255    0.023061   \n",
       "descr_RECKLESS ENDANGERMENT                         0.004675    0.001003   \n",
       "race_BLACK                                          0.531130    0.501003   \n",
       "rf_vcrim                                            0.259669    0.232620   \n",
       "pct_102                                             0.009775    0.004011   \n",
       "pct_50                                              0.009987    0.004011   \n",
       "descr_GRAND LARCENY                                 0.053230    0.039104   \n",
       "pct_79                                              0.021462    0.012032   \n",
       "pct_123                                             0.005844    0.011698   \n",
       "pct_19                                              0.007119    0.013703   \n",
       "pct_61                                              0.005631    0.011698   \n",
       "pct_22                                              0.004675    0.000334   \n",
       "pct_115                                             0.009137    0.016711   \n",
       "rf_furt                                             0.179027    0.208222   \n",
       "rf_rfcmp                                            0.070229    0.090241   \n",
       "pct_13                                              0.007012    0.014037   \n",
       "ac_proxm                                            0.364216    0.326872   \n",
       "pct_63                                              0.007969    0.001671   \n",
       "pct_105                                             0.015512    0.025735   \n",
       "rf_bulg                                             0.075967    0.098262   \n",
       "ac_cgdir                                            0.128772    0.157086   \n",
       "pct_72                                              0.007969    0.016377   \n",
       "trhsloc_Transit                                     0.038355    0.054479   \n",
       "descr_UNAUTHORIZED USE OF A VEHICLE                 0.001806    0.007353   \n",
       "pct_47                                              0.013600    0.024398   \n",
       "pct_40                                              0.020506    0.060829   \n",
       "descr_ROBBERY                                       0.164046    0.124666   \n",
       "pct_60                                              0.013706    0.049799   \n",
       "descr_THEFT OF SERVICES                             0.005950    0.021390   \n",
       "descr_CRIMINAL TRESPASS                             0.028581    0.185829   \n",
       "contrabn                                            0.005631    0.228275   \n",
       "pct_49                                              0.050255    0.031083   \n",
       "pf_hcuff                                            0.105397    0.333556   \n",
       "pistol                                              0.001594    0.051136   \n",
       "descr_CRIMINAL POSSESSION OF MARIHUANA              0.021037    0.052139   \n",
       "frisked                                             0.611772    0.728944   \n",
       "descr_GRAND LARCENY AUTO                            0.064917    0.036765   \n",
       "pf_grnd                                             0.008925    0.029746   \n",
       "knifcuti                                            0.007437    0.104612   \n",
       "pct_43                                              0.019550    0.039773   \n",
       "pct_42                                              0.010943    0.027406   \n",
       "searched                                            0.106354    0.654078   \n",
       "othrweap                                            0.005631    0.043449   \n",
       "pf_hands                                            0.103379    0.143382   \n",
       "sb_outln                                            0.008287    0.048128   \n",
       "descr_CRIMINAL POSSESION OF CONTROLLED SUBSTANCE    0.011687    0.024398   \n",
       "othpers                                             0.347535    0.247660   \n",
       "inout_Outside                                       0.846260    0.673797   \n",
       "trhsloc_Neither                                     0.877072    0.688837   \n",
       "typeofid_P                                          0.558542    0.620321   \n",
       "typeofid_R                                          0.055036    0.007019   \n",
       "pct_110                                             0.006269    0.020722   \n",
       "race_ASIAN/PACIFIC ISLANDER                         0.064598    0.043115   \n",
       "sb_admis                                            0.003612    0.054144   \n",
       "pct_106                                             0.118785    0.048797   \n",
       "cs_bulge                                            0.066086    0.090909   \n",
       "race_WHITE-HISPANIC                                 0.208989    0.262701   \n",
       "sb_other                                            0.058330    0.496992   \n",
       "city_QUEENS                                         0.275181    0.209225   \n",
       "cs_furtv                                            0.171908    0.205882   \n",
       "rf_othsw                                            0.200595    0.297794   \n",
       "ac_evasv                                            0.108054    0.148730   \n",
       "cs_drgtr                                            0.038037    0.092914   \n",
       "pct_76                                              0.007862    0.019051   \n",
       "pct_75                                              0.031024    0.012701   \n",
       "pct_67                                              0.026243    0.007353   \n",
       "cs_casng                                            0.146303    0.107286   \n",
       "descr_ASSAULT                                       0.101041    0.071524   \n",
       "sb_hdobj                                            0.043455    0.101604   \n",
       "descr_BURGLARY                                      0.105716    0.057821   \n",
       "descr_COMPUTER TRESPASS                             0.000106    0.004011   \n",
       "cs_objcs                                            0.023799    0.047126   \n",
       "descr_CPW                                           0.300786    0.236965   \n",
       "cs_descr                                            0.495113    0.404412   \n",
       "age                                                28.097535   28.726939   \n",
       "weight                                            169.810136  168.001337   \n",
       "\n",
       "                                                  test_stat   p-value  \n",
       "pct_20                                             1.961900  0.049800  \n",
       "pct_109                                            1.986800  0.046900  \n",
       "descr_CRIMINAL POSSESSION OF FORGED INSTRUMENT    -2.035400  0.041800  \n",
       "pct_94                                             2.087700  0.036800  \n",
       "pct_33                                            -2.128200  0.033300  \n",
       "race_OTHER                                         2.139700  0.032400  \n",
       "pct_9                                             -2.150500  0.031500  \n",
       "city_MANHATTAN                                    -2.250000  0.024400  \n",
       "month_October                                     -2.395200  0.016600  \n",
       "sex_Other                                          2.458100  0.014000  \n",
       "cs_cloth                                           2.485000  0.013000  \n",
       "pct_52                                             2.537000  0.011200  \n",
       "pf_ptwep                                          -2.543100  0.011000  \n",
       "cs_lkout                                           2.545900  0.010900  \n",
       "ac_rept                                            2.555200  0.010600  \n",
       "typeofid_V                                         2.567700  0.010200  \n",
       "sex_Male                                           2.590100  0.009600  \n",
       "explnstp                                           2.595300  0.009500  \n",
       "pct_32                                            -2.595400  0.009400  \n",
       "descr_PETIT LARCENY                               -2.600500  0.009300  \n",
       "pct_104                                            2.632400  0.008500  \n",
       "pct_84                                             2.676400  0.007400  \n",
       "pct_44                                             2.763500  0.005700  \n",
       "pct_114                                            2.811400  0.004900  \n",
       "descr_RECKLESS ENDANGERMENT                        2.847900  0.004400  \n",
       "race_BLACK                                         2.874300  0.004000  \n",
       "rf_vcrim                                           2.964000  0.003000  \n",
       "pct_102                                            3.012000  0.002600  \n",
       "pct_50                                             3.093700  0.002000  \n",
       "descr_GRAND LARCENY                                3.093400  0.002000  \n",
       "pct_79                                             3.275300  0.001100  \n",
       "pct_123                                           -3.286700  0.001000  \n",
       "pct_19                                            -3.377100  0.000700  \n",
       "pct_61                                            -3.444200  0.000600  \n",
       "pct_22                                             3.440000  0.000600  \n",
       "pct_115                                           -3.465500  0.000500  \n",
       "rf_furt                                           -3.574500  0.000400  \n",
       "rf_rfcmp                                          -3.618800  0.000300  \n",
       "pct_13                                            -3.603000  0.000300  \n",
       "ac_proxm                                           3.718000  0.000200  \n",
       "pct_63                                             3.748400  0.000200  \n",
       "pct_105                                           -3.666000  0.000200  \n",
       "rf_bulg                                           -3.886100  0.000100  \n",
       "ac_cgdir                                          -3.940500  0.000100  \n",
       "pct_72                                            -4.027300  0.000100  \n",
       "trhsloc_Transit                                   -3.819300  0.000100  \n",
       "descr_UNAUTHORIZED USE OF A VEHICLE               -4.720700  0.000000  \n",
       "pct_47                                            -4.075200  0.000000  \n",
       "pct_40                                           -11.220900  0.000000  \n",
       "descr_ROBBERY                                      5.190900  0.000000  \n",
       "pct_60                                           -11.618500  0.000000  \n",
       "descr_THEFT OF SERVICES                           -7.516300  0.000000  \n",
       "descr_CRIMINAL TRESPASS                          -30.069500  0.000000  \n",
       "contrabn                                         -44.903200  0.000000  \n",
       "pct_49                                             4.377500  0.000000  \n",
       "pf_hcuff                                         -29.621300  0.000000  \n",
       "pistol                                           -20.422400  0.000000  \n",
       "descr_CRIMINAL POSSESSION OF MARIHUANA            -8.900100  0.000000  \n",
       "frisked                                          -11.631400  0.000000  \n",
       "descr_GRAND LARCENY AUTO                           5.732900  0.000000  \n",
       "pf_grnd                                           -8.459700  0.000000  \n",
       "knifcuti                                         -26.766200  0.000000  \n",
       "pct_43                                            -6.242000  0.000000  \n",
       "pct_42                                            -6.471500  0.000000  \n",
       "searched                                         -61.240900  0.000000  \n",
       "othrweap                                         -14.945900  0.000000  \n",
       "pf_hands                                          -6.019900  0.000000  \n",
       "sb_outln                                         -14.318500  0.000000  \n",
       "descr_CRIMINAL POSSESION OF CONTROLLED SUBSTANCE  -5.023500  0.000000  \n",
       "othpers                                           10.172900  0.000000  \n",
       "inout_Outside                                     20.727000  0.000000  \n",
       "trhsloc_Neither                                   23.970800  0.000000  \n",
       "typeofid_P                                        -5.951800  0.000000  \n",
       "typeofid_R                                        11.222100  0.000000  \n",
       "pct_110                                           -7.006900  0.000000  \n",
       "race_ASIAN/PACIFIC ISLANDER                        4.330000  0.000000  \n",
       "sb_admis                                         -19.307200  0.000000  \n",
       "pct_106                                           11.023200  0.000000  \n",
       "cs_bulge                                          -4.573600  0.000000  \n",
       "race_WHITE-HISPANIC                               -6.158600  0.000000  \n",
       "sb_other                                         -56.428200  0.000000  \n",
       "city_QUEENS                                        7.171100  0.000000  \n",
       "cs_furtv                                          -4.212600  0.000000  \n",
       "rf_othsw                                         -11.107600  0.000000  \n",
       "ac_evasv                                          -6.010700  0.000000  \n",
       "cs_drgtr                                         -11.855500  0.000000  \n",
       "pct_76                                            -5.215100  0.000000  \n",
       "pct_75                                             5.425400  0.000000  \n",
       "pct_67                                             6.179300  0.000000  \n",
       "cs_casng                                           5.408400  0.000000  \n",
       "descr_ASSAULT                                      4.821100  0.000000  \n",
       "sb_hdobj                                         -11.903500  0.000000  \n",
       "descr_BURGLARY                                     7.813900  0.000000  \n",
       "descr_COMPUTER TRESPASS                           -5.749600  0.000000  \n",
       "cs_objcs                                          -6.576700  0.000000  \n",
       "descr_CPW                                          6.733600  0.000000  \n",
       "cs_descr                                           8.655800  0.000000  \n",
       "age                                               -2.554680  0.010658  \n",
       "weight                                             2.675898  0.007477  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_keep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12404, 99)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features_selected.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Feature Elimination with Smaller Feature Set\n",
    "The smaller feature set did perform just about as well as previous models, although, the number of features is still fairly high for interpretability. As such, the number of features will be trimmed again using RFECV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=10,\n",
       "      estimator=LogisticRegression(class_weight='balanced', max_iter=1000))"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "selector = RFECV(LogisticRegression(class_weight='balanced', max_iter=1000), step=1, cv=10)\n",
    "selector.fit(X_train_sel, y_train_sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(class_weight='balanced', max_iter=1000)\n",
      "15\n",
      "[38 75 11 54 55 45 49 50 59 23 66 32 71 53 70 42 39  1 37  8 74 57 15 31\n",
      "  1 79 52 65 13 46 51  1 25 22 17 29 63 67  5 81 14 26 34 68  4 28  1 72\n",
      "  6 78  2  1  1  1 44  3  1  9 35 62 20  1 84  7  1  1 82 36 76 58 80 27\n",
      " 43  1  1 77 24 16 33 60 12 48 40 69 73 21 19 10 47 30 83  1 64  1 61 18\n",
      " 85 56 41]\n",
      "[False False False False False False False False False False False False\n",
      " False False False False False  True False False False False False False\n",
      "  True False False False False False False  True False False False False\n",
      " False False False False False False False False False False  True False\n",
      " False False False  True  True  True False False  True False False False\n",
      " False  True False False  True  True False False False False False False\n",
      " False  True  True False False False False False False False False False\n",
      " False False False False False False False  True False  True False False\n",
      " False False False]\n"
     ]
    }
   ],
   "source": [
    "print(selector.estimator_)\n",
    "print(selector.n_features_)\n",
    "print(selector.ranking_)\n",
    "print(selector.support_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the fitted scaler, transform the training feature set to values between 0 and 1\n",
    "X_train_rfe = selector.transform(X_train_sel)  \n",
    "\n",
    "# Transform the testing set as well to ensure consistency in predictions across feature sets\n",
    "X_test_rfe = selector.transform(X_test_sel) \n",
    "\n",
    "# Predict on the training set\n",
    "rfe_train_preds = selector.estimator_.predict(X_train_rfe)\n",
    "\n",
    "# Predict on the testing set\n",
    "rfe_test_preds = selector.estimator_.predict(X_test_rfe)\n",
    "\n",
    "# Train Metrics\n",
    "basic_lr_sel_rfe_accuracy = metrics.accuracy_score(y_train_sel, rfe_train_preds)\n",
    "basic_lr_sel_rfe_precision = metrics.precision_score(y_train_sel, rfe_train_preds)\n",
    "basic_lr_sel_rfe_recall = metrics.recall_score(y_train_sel, rfe_train_preds)\n",
    "basic_lr_sel_rfe_f1 = metrics.f1_score(y_train_sel, rfe_train_preds)\n",
    "\n",
    "\n",
    "# Test Metrics\n",
    "basic_lr_sel_rfe_test_accuracy = metrics.accuracy_score(y_test_sel, rfe_test_preds)\n",
    "basic_lr_sel_rfe_test_precision = metrics.precision_score(y_test_sel, rfe_test_preds)\n",
    "basic_lr_sel_rfe_test_recall = metrics.recall_score(y_test_sel, rfe_test_preds)\n",
    "basic_lr_sel_rfe_test_f1 = metrics.f1_score(y_test_sel, rfe_test_preds)\n",
    "\n",
    "performance.append({'model': 'Basic LogReg w/ Selected Features & RFECV', '# Features': X_train_rfe.shape[1], 'Train Accuracy': basic_lr_sel_rfe_accuracy,\n",
    "                    'Test Accuracy': basic_lr_sel_rfe_test_accuracy, 'Train Recall': basic_lr_sel_rfe_recall,\n",
    "                    'Test Recall': basic_lr_sel_rfe_test_recall, 'Train Precision': basic_lr_sel_rfe_precision,\n",
    "                    'Test Precision': basic_lr_sel_rfe_test_precision, 'Train F1': basic_lr_sel_rfe_f1,\n",
    "                    'Test F1': basic_lr_sel_rfe_test_f1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       model  # Features  Train Accuracy  \\\n",
       "0                  Basic Logistic Regression         231        0.861635   \n",
       "1           GridSearchCV Logistic Regression         231        0.860425   \n",
       "2         Basic Logistic Regression Selected          99        0.859216   \n",
       "3  Basic LogReg w/ Selected Features & RFECV          15        0.860828   \n",
       "\n",
       "   Test Accuracy  Train Recall  Test Recall  Train Precision  Test Precision  \\\n",
       "0       0.858525      0.801843     0.796694         0.680171        0.678873   \n",
       "1       0.855300      0.788018     0.796694         0.681522        0.674221   \n",
       "2       0.858928      0.795140     0.793388         0.676408        0.680851   \n",
       "3       0.863362      0.760369     0.780165         0.691692        0.696165   \n",
       "\n",
       "   Train F1   Test F1  \n",
       "0  0.736012  0.733080  \n",
       "1  0.730911  0.726163  \n",
       "2  0.730984  0.732824  \n",
       "3  0.724406  0.735776  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False False False False False False False\n",
      " False False False False False  True False False False False False False\n",
      "  True False False False False False False  True False False False False\n",
      " False False False False False False False False False False  True False\n",
      " False False False  True  True  True False False  True False False False\n",
      " False  True False False  True  True False False False False False False\n",
      " False  True  True False False False False False False False False False\n",
      " False False False False False False False  True False  True False False\n",
      " False False False]\n"
     ]
    }
   ],
   "source": [
    "print(selector.support_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a list of selected feature indices. In the list above, True denotes a feature that was selected to \n",
    "# bw included in the model. I'll add each of these indices to a list to later parse out ans explore. \n",
    "index_sel = []\n",
    "for i in range(len(selector.support_)):\n",
    "    if selector.support_[i]==True:\n",
    "        index_sel.append(i)\n",
    "    else:\n",
    "        continue\n",
    "\n",
    "rfecv_feats = list(df_features.iloc[:, index_sel].columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After recursive feature elimination, the top 15 features that remeained in the model were:\n",
    "- **pf_drwep**: Police force used - weapon drawn\n",
    "- **ac_inves**: Additional Circumstances - ongoing investivation (i.e., the person was potentially a suspect in an ongoing investigation)\n",
    "- **cs_casng**: Reason for stop - casing a victim or location\n",
    "- **ac_time**: Additional Circumstances - time of day fits additional crime incidence\n",
    "- **sb_outln**: Basis of search - outline of weapon\n",
    "- **sb_admis**: Basis of search - admission by suspect\n",
    "- **sb_other**: Basis of search - other\n",
    "- **age**: suspect's age was a strong predictor of arrest. Younger individuals were more likely to be stopped and arrested.\n",
    "- **pct_7**, **pct_13**, **pct_14**, **pct_25**, **pct_26**, **pct_52**, **pct_61**: The precint nearest to the incident. These precincts must have higher arrest rates as a proportion of SQF incidents.\n",
    "\n",
    "Let's take a similar approach to modeling with Decision Trees and see which features come out on top. We can then conduct some further interactions with the best performing features to observe performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a baseline decision tree model using the original feature set\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
    "\n",
    "# Instantiate the decision tree classifier using all default parameters and class weight balanced due to class \n",
    "# imbalance\n",
    "clf = DecisionTreeClassifier(class_weight='balanced', random_state=1)\n",
    "\n",
    "# Fit the model to our training data\n",
    "clf_basic = clf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions for y_values in the training set\n",
    "dtc_basic_train_preds = clf_basic.predict(X_train)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "dtc_basic_test_preds = clf_basic.predict(X_test)\n",
    "\n",
    "# Train Metrics\n",
    "dtc_basic_train_accuracy = metrics.accuracy_score(y_train, dtc_basic_train_preds)\n",
    "dtc_basic_train_precision = metrics.precision_score(y_train, dtc_basic_train_preds)\n",
    "dtc_basic_train_recall = metrics.recall_score(y_train, dtc_basic_train_preds)\n",
    "dtc_basic_train_f1 = metrics.f1_score(y_train, dtc_basic_train_preds)\n",
    "\n",
    "\n",
    "# Test Metrics\n",
    "dtc_basic_accuracy = metrics.accuracy_score(y_test, dtc_basic_test_preds)\n",
    "dtc_basic_precision = metrics.precision_score(y_test, dtc_basic_test_preds)\n",
    "dtc_basic_recall = metrics.recall_score(y_test, dtc_basic_test_preds)\n",
    "dtc_basic_f1 = metrics.f1_score(y_test, dtc_basic_test_preds)\n",
    "\n",
    "\n",
    "performance.append({'model': 'Basic Decision Tree Model', '# Features': X_train.shape[1], 'Train Accuracy': dtc_basic_train_accuracy,\n",
    "                    'Test Accuracy': dtc_basic_accuracy, 'Train Recall': dtc_basic_train_recall,\n",
    "                    'Test Recall': dtc_basic_recall, 'Train Precision': dtc_basic_train_precision,\n",
    "                    'Test Precision': dtc_basic_precision, 'Train F1': dtc_basic_train_f1,\n",
    "                    'Test F1': dtc_basic_f1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       model  # Features  Train Accuracy  \\\n",
       "0                  Basic Logistic Regression         231        0.861635   \n",
       "1           GridSearchCV Logistic Regression         231        0.860425   \n",
       "2         Basic Logistic Regression Selected          99        0.859216   \n",
       "3  Basic LogReg w/ Selected Features & RFECV          15        0.860828   \n",
       "4                  Basic Decision Tree Model         231        1.000000   \n",
       "\n",
       "   Test Accuracy  Train Recall  Test Recall  Train Precision  Test Precision  \\\n",
       "0       0.858525      0.801843     0.796694         0.680171        0.678873   \n",
       "1       0.855300      0.788018     0.796694         0.681522        0.674221   \n",
       "2       0.858928      0.795140     0.793388         0.676408        0.680851   \n",
       "3       0.863362      0.760369     0.780165         0.691692        0.696165   \n",
       "4       0.851270      1.000000     0.700826         1.000000        0.692810   \n",
       "\n",
       "   Train F1   Test F1  \n",
       "0  0.736012  0.733080  \n",
       "1  0.730911  0.726163  \n",
       "2  0.730984  0.732824  \n",
       "3  0.724406  0.735776  \n",
       "4  1.000000  0.696795  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It seems the Decision Tree Severly overfit to the training data. A GridSearch decision tree will be tested to \n",
    "# see if this improves performance.\n",
    "\n",
    "dt_params = {'max_depth': [2, 3, 5, 10, 15, 20],\n",
    "              'criterion': ['gini', 'entropy'],\n",
    "              'min_samples_leaf': [2,5,10],\n",
    "              'min_samples_split': [5,7,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:    7.1s\n",
      "[Parallel(n_jobs=-1)]: Done 426 tasks      | elapsed:   25.2s\n",
      "[Parallel(n_jobs=-1)]: Done 776 tasks      | elapsed:   40.0s\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:   56.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5, 10, 15, 20],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [5, 7, 10]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the instance\n",
    "grid_dt_basic = GridSearchCV(clf, dt_params, cv=10, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "# Fit the decision tree classifier to the training data\n",
    "grid_dt_basic.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.725267\n",
      "Best Params: {'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5}\n"
     ]
    }
   ],
   "source": [
    "# Show the parameters for the best model and the f1 score for the best model\n",
    "print('Best Score: %f' % (grid_dt_basic.best_score_))\n",
    "print('Best Params:', grid_dt_basic.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for y values in the training set\n",
    "dtc_grid_train_preds = grid_dt_basic.best_estimator_.predict(X_train)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "dtc_grid_test_preds = grid_dt_basic.best_estimator_.predict(X_test)\n",
    "\n",
    "# Train Metrics\n",
    "dtc_grid_train_accuracy = metrics.accuracy_score(y_train, dtc_grid_train_preds)\n",
    "dtc_grid_train_precision = metrics.precision_score(y_train, dtc_grid_train_preds)\n",
    "dtc_grid_train_recall = metrics.recall_score(y_train, dtc_grid_train_preds)\n",
    "dtc_grid_train_f1 = metrics.f1_score(y_train, dtc_grid_train_preds)\n",
    "\n",
    "\n",
    "# Test Metrics\n",
    "dtc_grid_accuracy = metrics.accuracy_score(y_test, dtc_grid_test_preds)\n",
    "dtc_grid_precision = metrics.precision_score(y_test, dtc_grid_test_preds)\n",
    "dtc_grid_recall = metrics.recall_score(y_test, dtc_grid_test_preds)\n",
    "dtc_grid_f1 = metrics.f1_score(y_test, dtc_grid_test_preds)\n",
    "\n",
    "\n",
    "performance.append({'model': 'GridSearchCV Decision Tree Model', '# Features': X_train.shape[1], 'Train Accuracy': dtc_grid_train_accuracy,\n",
    "                    'Test Accuracy': dtc_grid_accuracy, 'Train Recall': dtc_grid_train_recall,\n",
    "                    'Test Recall': dtc_grid_recall, 'Train Precision': dtc_grid_train_precision,\n",
    "                    'Test Precision': dtc_grid_precision, 'Train F1': dtc_grid_train_f1,\n",
    "                    'Test F1': dtc_grid_f1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       model  # Features  Train Accuracy  \\\n",
       "0                  Basic Logistic Regression         231        0.861635   \n",
       "1           GridSearchCV Logistic Regression         231        0.860425   \n",
       "2         Basic Logistic Regression Selected          99        0.859216   \n",
       "3  Basic LogReg w/ Selected Features & RFECV          15        0.860828   \n",
       "4                  Basic Decision Tree Model         231        1.000000   \n",
       "5           GridSearchCV Decision Tree Model         231        0.905271   \n",
       "\n",
       "   Test Accuracy  Train Recall  Test Recall  Train Precision  Test Precision  \\\n",
       "0       0.858525      0.801843     0.796694         0.680171        0.678873   \n",
       "1       0.855300      0.788018     0.796694         0.681522        0.674221   \n",
       "2       0.858928      0.795140     0.793388         0.676408        0.680851   \n",
       "3       0.863362      0.760369     0.780165         0.691692        0.696165   \n",
       "4       0.851270      1.000000     0.700826         1.000000        0.692810   \n",
       "5       0.875050      0.803519     0.740496         0.802846        0.745424   \n",
       "\n",
       "   Train F1   Test F1  \n",
       "0  0.736012  0.733080  \n",
       "1  0.730911  0.726163  \n",
       "2  0.730984  0.732824  \n",
       "3  0.724406  0.735776  \n",
       "4  1.000000  0.696795  \n",
       "5  0.803183  0.742952  "
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "### Significant Features Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a baseline decision tree model using the significant feature set\n",
    "\n",
    "# Fit the model to our training data\n",
    "clf_basic_sel = clf.fit(X_train_sel, y_train_sel)\n",
    "\n",
    "# Make predictions for y_values in the training set\n",
    "dtc_basic_train_preds_sel = clf_basic_sel.predict(X_train_sel)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "dtc_basic_test_preds_sel = clf_basic_sel.predict(X_test_sel)\n",
    "\n",
    "# Train Metrics\n",
    "dtc_basic_train_accuracy_sel = metrics.accuracy_score(y_train_sel, dtc_basic_train_preds_sel)\n",
    "dtc_basic_train_precision_sel = metrics.precision_score(y_train_sel, dtc_basic_train_preds_sel)\n",
    "dtc_basic_train_recall_sel = metrics.recall_score(y_train_sel, dtc_basic_train_preds_sel)\n",
    "dtc_basic_train_f1_sel = metrics.f1_score(y_train_sel, dtc_basic_train_preds_sel)\n",
    "\n",
    "# Test Metrics\n",
    "dtc_basic_accuracy_sel = metrics.accuracy_score(y_test_sel, dtc_basic_test_preds_sel)\n",
    "dtc_basic_precision_sel = metrics.precision_score(y_test_sel, dtc_basic_test_preds_sel)\n",
    "dtc_basic_recall_sel = metrics.recall_score(y_test_sel, dtc_basic_test_preds_sel)\n",
    "dtc_basic_f1_sel = metrics.f1_score(y_test_sel, dtc_basic_test_preds_sel)\n",
    "\n",
    "performance.append({'model': 'Basic Decision Tree Selected Features', '# Features': X_train_sel.shape[1], 'Train Accuracy': dtc_basic_train_accuracy_sel,\n",
    "                    'Test Accuracy': dtc_basic_accuracy_sel, 'Train Recall': dtc_basic_train_recall_sel,\n",
    "                    'Test Recall': dtc_basic_recall_sel, 'Train Precision': dtc_basic_train_precision_sel,\n",
    "                    'Test Precision': dtc_basic_precision_sel, 'Train F1': dtc_basic_train_f1_sel,\n",
    "                    'Test F1': dtc_basic_f1_sel})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       model  # Features  Train Accuracy  \\\n",
       "0                  Basic Logistic Regression         231        0.861635   \n",
       "1           GridSearchCV Logistic Regression         231        0.860425   \n",
       "2         Basic Logistic Regression Selected          99        0.859216   \n",
       "3  Basic LogReg w/ Selected Features & RFECV          15        0.860828   \n",
       "4                  Basic Decision Tree Model         231        1.000000   \n",
       "5           GridSearchCV Decision Tree Model         231        0.905271   \n",
       "6      Basic Decision Tree Selected Features          99        1.000000   \n",
       "\n",
       "   Test Accuracy  Train Recall  Test Recall  Train Precision  Test Precision  \\\n",
       "0       0.858525      0.801843     0.796694         0.680171        0.678873   \n",
       "1       0.855300      0.788018     0.796694         0.681522        0.674221   \n",
       "2       0.858928      0.795140     0.793388         0.676408        0.680851   \n",
       "3       0.863362      0.760369     0.780165         0.691692        0.696165   \n",
       "4       0.851270      1.000000     0.700826         1.000000        0.692810   \n",
       "5       0.875050      0.803519     0.740496         0.802846        0.745424   \n",
       "6       0.836356      1.000000     0.676033         1.000000        0.660743   \n",
       "\n",
       "   Train F1   Test F1  \n",
       "0  0.736012  0.733080  \n",
       "1  0.730911  0.726163  \n",
       "2  0.730984  0.732824  \n",
       "3  0.724406  0.735776  \n",
       "4  1.000000  0.696795  \n",
       "5  0.803183  0.742952  \n",
       "6  1.000000  0.668301  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridsearchCV Decision Tree with Smaller Feature Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Done 328 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=-1)]: Done 828 tasks      | elapsed:   13.5s\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:   19.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5, 10, 15, 20],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [5, 7, 10]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the instance\n",
    "grid_dt_sel = GridSearchCV(clf, dt_params, cv=10, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "# Fit the decision tree classifier to the training data\n",
    "grid_dt_sel.fit(X_train_sel, y_train_sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.716587\n",
      "Best Params: {'criterion': 'entropy', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 7}\n"
     ]
    }
   ],
   "source": [
    "# Show the parameters for the best model and the f1 score for the best model\n",
    "print('Best Score: %f' % (grid_dt_sel.best_score_))\n",
    "print('Best Params:', grid_dt_sel.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for y values in the training set\n",
    "dtc_grid_train_preds_sel = grid_dt_sel.best_estimator_.predict(X_train_sel)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "dtc_grid_test_preds_sel = grid_dt_sel.best_estimator_.predict(X_test_sel)\n",
    "\n",
    "# Train Metrics\n",
    "dtc_grid_train_accuracy_sel = metrics.accuracy_score(y_train_sel, dtc_grid_train_preds_sel)\n",
    "dtc_grid_train_precision_sel = metrics.precision_score(y_train_sel, dtc_grid_train_preds_sel)\n",
    "dtc_grid_train_recall_sel = metrics.recall_score(y_train_sel, dtc_grid_train_preds_sel)\n",
    "dtc_grid_train_f1_sel = metrics.f1_score(y_train_sel, dtc_grid_train_preds_sel)\n",
    "\n",
    "# Test Metrics\n",
    "dtc_grid_accuracy_sel = metrics.accuracy_score(y_test_sel, dtc_grid_test_preds_sel)\n",
    "dtc_grid_precision_sel = metrics.precision_score(y_test_sel, dtc_grid_test_preds_sel)\n",
    "dtc_grid_recall_sel = metrics.recall_score(y_test_sel, dtc_grid_test_preds_sel)\n",
    "dtc_grid_f1_sel = metrics.f1_score(y_test_sel, dtc_grid_test_preds_sel)\n",
    "\n",
    "\n",
    "performance.append({'model': 'GridSearchCV Decision Tree Model w/ Selected Features', '# Features': X_train_sel.shape[1], 'Train Accuracy': dtc_grid_train_accuracy_sel,\n",
    "                    'Test Accuracy': dtc_grid_accuracy_sel, 'Train Recall': dtc_grid_train_recall_sel,\n",
    "                    'Test Recall': dtc_grid_recall_sel, 'Train Precision': dtc_grid_train_precision_sel,\n",
    "                    'Test Precision': dtc_grid_precision_sel, 'Train F1': dtc_grid_train_f1_sel,\n",
    "                    'Test F1': dtc_grid_f1_sel})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               model  # Features  \\\n",
       "0                          Basic Logistic Regression         231   \n",
       "1                   GridSearchCV Logistic Regression         231   \n",
       "2                 Basic Logistic Regression Selected          99   \n",
       "3          Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                          Basic Decision Tree Model         231   \n",
       "5                   GridSearchCV Decision Tree Model         231   \n",
       "6              Basic Decision Tree Selected Features          99   \n",
       "7  GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "\n",
       "   Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0        0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1        0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2        0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3        0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4        1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5        0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6        1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7        0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "\n",
       "   Test Precision  Train F1   Test F1  \n",
       "0        0.678873  0.736012  0.733080  \n",
       "1        0.674221  0.730911  0.726163  \n",
       "2        0.680851  0.730984  0.732824  \n",
       "3        0.696165  0.724406  0.735776  \n",
       "4        0.692810  1.000000  0.696795  \n",
       "5        0.745424  0.803183  0.742952  \n",
       "6        0.660743  1.000000  0.668301  \n",
       "7        0.727124  0.780190  0.731306  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_tuples = list(zip(list(df_features_selected.columns), list(grid_dt_sel.best_estimator_.feature_importances_)))\n",
    "grid_feat_perf = pd.DataFrame(list_of_tuples, columns = ['features', 'feature_importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_feat_perf = grid_feat_perf.sort_values(by='feature_importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_feat_perf.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "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>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>features</th>\n",
       "      <th>feature_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>searched</td>\n",
       "      <td>4.852550e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>contrabn</td>\n",
       "      <td>1.140421e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>descr_CRIMINAL TRESPASS</td>\n",
       "      <td>7.484658e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>pf_hcuff</td>\n",
       "      <td>3.385230e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>sb_hdobj</td>\n",
       "      <td>2.571661e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>knifcuti</td>\n",
       "      <td>2.313153e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>age</td>\n",
       "      <td>2.048928e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>pistol</td>\n",
       "      <td>1.823258e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>pct_106</td>\n",
       "      <td>1.480994e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>weight</td>\n",
       "      <td>1.352497e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>descr_THEFT OF SERVICES</td>\n",
       "      <td>1.168689e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>descr_CPW</td>\n",
       "      <td>1.140109e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>othrweap</td>\n",
       "      <td>9.519408e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>trhsloc_Neither</td>\n",
       "      <td>9.227993e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>descr_PETIT LARCENY</td>\n",
       "      <td>9.091511e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>trhsloc_Transit</td>\n",
       "      <td>7.878902e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>cs_descr</td>\n",
       "      <td>6.318541e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>race_WHITE-HISPANIC</td>\n",
       "      <td>6.025353e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>frisked</td>\n",
       "      <td>5.975477e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>typeofid_R</td>\n",
       "      <td>5.466149e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>race_BLACK</td>\n",
       "      <td>5.443841e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>rf_vcrim</td>\n",
       "      <td>5.302435e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>ac_evasv</td>\n",
       "      <td>4.811385e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>rf_furt</td>\n",
       "      <td>4.675333e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>rf_bulg</td>\n",
       "      <td>4.491476e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>pct_19</td>\n",
       "      <td>3.800162e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>city_MANHATTAN</td>\n",
       "      <td>3.725324e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>typeofid_P</td>\n",
       "      <td>3.536960e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>cs_bulge</td>\n",
       "      <td>3.311725e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>ac_rept</td>\n",
       "      <td>2.810217e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>pct_75</td>\n",
       "      <td>2.677088e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>inout_Outside</td>\n",
       "      <td>2.511523e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>typeofid_V</td>\n",
       "      <td>2.474588e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>pct_110</td>\n",
       "      <td>2.337363e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>descr_RECKLESS ENDANGERMENT</td>\n",
       "      <td>2.318013e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>pf_hands</td>\n",
       "      <td>2.238276e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>pf_grnd</td>\n",
       "      <td>2.224595e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>cs_drgtr</td>\n",
       "      <td>2.204195e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>cs_casng</td>\n",
       "      <td>2.104992e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>descr_CRIMINAL POSSESSION OF MARIHUANA</td>\n",
       "      <td>1.985176e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>pct_67</td>\n",
       "      <td>1.951346e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>sb_outln</td>\n",
       "      <td>1.815288e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>rf_othsw</td>\n",
       "      <td>1.811399e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>othpers</td>\n",
       "      <td>1.776940e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>sb_other</td>\n",
       "      <td>1.761643e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>rf_rfcmp</td>\n",
       "      <td>1.680958e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>pct_49</td>\n",
       "      <td>1.437526e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>cs_furtv</td>\n",
       "      <td>1.425016e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>pct_94</td>\n",
       "      <td>1.221419e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>descr_ROBBERY</td>\n",
       "      <td>1.195708e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>pct_115</td>\n",
       "      <td>1.186015e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>sex_Male</td>\n",
       "      <td>1.185055e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>descr_CRIMINAL POSSESION OF CONTROLLED SUBSTANCE</td>\n",
       "      <td>1.173172e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>descr_GRAND LARCENY</td>\n",
       "      <td>9.896240e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>pct_123</td>\n",
       "      <td>9.600036e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>pct_60</td>\n",
       "      <td>9.262888e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>pct_52</td>\n",
       "      <td>8.612596e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>pct_84</td>\n",
       "      <td>7.925098e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>cs_objcs</td>\n",
       "      <td>7.746371e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>pct_43</td>\n",
       "      <td>7.550383e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>sb_admis</td>\n",
       "      <td>7.282108e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>pct_61</td>\n",
       "      <td>7.221348e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>city_QUEENS</td>\n",
       "      <td>5.346558e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>pct_40</td>\n",
       "      <td>4.543948e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>explnstp</td>\n",
       "      <td>4.028746e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>pct_105</td>\n",
       "      <td>6.214462e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>month_October</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>sex_Other</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>descr_ASSAULT</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>pct_33</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>cs_cloth</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>descr_CRIMINAL POSSESSION OF FORGED INSTRUMENT</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>pct_76</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>pct_9</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>pf_ptwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>cs_lkout</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>descr_BURGLARY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>descr_COMPUTER TRESPASS</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>race_OTHER</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>pct_44</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>pct_32</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>pct_47</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>descr_UNAUTHORIZED USE OF A VEHICLE</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>pct_72</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>ac_cgdir</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>pct_63</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>descr_GRAND LARCENY AUTO</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>ac_proxm</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>pct_42</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>pct_104</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>pct_22</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>pct_79</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>pct_50</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>pct_102</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>pct_114</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>pct_109</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>race_ASIAN/PACIFIC ISLANDER</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>pct_20</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    level_0  index                                          features  \\\n",
       "0        64     64                                          searched   \n",
       "1        53     53                                          contrabn   \n",
       "2        52     52                           descr_CRIMINAL TRESPASS   \n",
       "3        55     55                                          pf_hcuff   \n",
       "4        91     91                                          sb_hdobj   \n",
       "5        61     61                                          knifcuti   \n",
       "6        97     97                                               age   \n",
       "7        56     56                                            pistol   \n",
       "8        77     77                                           pct_106   \n",
       "9        98     98                                            weight   \n",
       "10       51     51                           descr_THEFT OF SERVICES   \n",
       "11       95     95                                         descr_CPW   \n",
       "12       65     65                                          othrweap   \n",
       "13       71     71                                   trhsloc_Neither   \n",
       "14       19     19                               descr_PETIT LARCENY   \n",
       "15       45     45                                   trhsloc_Transit   \n",
       "16       96     96                                          cs_descr   \n",
       "17       79     79                               race_WHITE-HISPANIC   \n",
       "18       58     58                                           frisked   \n",
       "19       73     73                                        typeofid_R   \n",
       "20       25     25                                        race_BLACK   \n",
       "21       26     26                                          rf_vcrim   \n",
       "22       84     84                                          ac_evasv   \n",
       "23       36     36                                           rf_furt   \n",
       "24       42     42                                           rf_bulg   \n",
       "25       32     32                                            pct_19   \n",
       "26        7      7                                    city_MANHATTAN   \n",
       "27       72     72                                        typeofid_P   \n",
       "28       78     78                                          cs_bulge   \n",
       "29       14     14                                           ac_rept   \n",
       "30       87     87                                            pct_75   \n",
       "31       70     70                                     inout_Outside   \n",
       "32       15     15                                        typeofid_V   \n",
       "33       74     74                                           pct_110   \n",
       "34       24     24                       descr_RECKLESS ENDANGERMENT   \n",
       "35       66     66                                          pf_hands   \n",
       "36       60     60                                           pf_grnd   \n",
       "37       85     85                                          cs_drgtr   \n",
       "38       89     89                                          cs_casng   \n",
       "39       57     57            descr_CRIMINAL POSSESSION OF MARIHUANA   \n",
       "40       88     88                                            pct_67   \n",
       "41       67     67                                          sb_outln   \n",
       "42       83     83                                          rf_othsw   \n",
       "43       69     69                                           othpers   \n",
       "44       80     80                                          sb_other   \n",
       "45       37     37                                          rf_rfcmp   \n",
       "46       54     54                                            pct_49   \n",
       "47       82     82                                          cs_furtv   \n",
       "48        3      3                                            pct_94   \n",
       "49       49     49                                     descr_ROBBERY   \n",
       "50       35     35                                           pct_115   \n",
       "51       16     16                                          sex_Male   \n",
       "52       68     68  descr_CRIMINAL POSSESION OF CONTROLLED SUBSTANCE   \n",
       "53       29     29                               descr_GRAND LARCENY   \n",
       "54       31     31                                           pct_123   \n",
       "55       50     50                                            pct_60   \n",
       "56       11     11                                            pct_52   \n",
       "57       21     21                                            pct_84   \n",
       "58       94     94                                          cs_objcs   \n",
       "59       62     62                                            pct_43   \n",
       "60       76     76                                          sb_admis   \n",
       "61       33     33                                            pct_61   \n",
       "62       81     81                                       city_QUEENS   \n",
       "63       48     48                                            pct_40   \n",
       "64       17     17                                          explnstp   \n",
       "65       41     41                                           pct_105   \n",
       "66        8      8                                     month_October   \n",
       "67        9      9                                         sex_Other   \n",
       "68       90     90                                     descr_ASSAULT   \n",
       "69        4      4                                            pct_33   \n",
       "70       10     10                                          cs_cloth   \n",
       "71        2      2    descr_CRIMINAL POSSESSION OF FORGED INSTRUMENT   \n",
       "72       86     86                                            pct_76   \n",
       "73        6      6                                             pct_9   \n",
       "74       12     12                                          pf_ptwep   \n",
       "75       13     13                                          cs_lkout   \n",
       "76       92     92                                    descr_BURGLARY   \n",
       "77       93     93                           descr_COMPUTER TRESPASS   \n",
       "78        5      5                                        race_OTHER   \n",
       "79       22     22                                            pct_44   \n",
       "80       18     18                                            pct_32   \n",
       "81       38     38                                            pct_13   \n",
       "82       47     47                                            pct_47   \n",
       "83       46     46               descr_UNAUTHORIZED USE OF A VEHICLE   \n",
       "84       44     44                                            pct_72   \n",
       "85       43     43                                          ac_cgdir   \n",
       "86       40     40                                            pct_63   \n",
       "87       59     59                          descr_GRAND LARCENY AUTO   \n",
       "88       39     39                                          ac_proxm   \n",
       "89       63     63                                            pct_42   \n",
       "90       20     20                                           pct_104   \n",
       "91       34     34                                            pct_22   \n",
       "92       30     30                                            pct_79   \n",
       "93       28     28                                            pct_50   \n",
       "94       27     27                                           pct_102   \n",
       "95       23     23                                           pct_114   \n",
       "96        1      1                                           pct_109   \n",
       "97       75     75                       race_ASIAN/PACIFIC ISLANDER   \n",
       "98        0      0                                            pct_20   \n",
       "\n",
       "    feature_importance  \n",
       "0         4.852550e-01  \n",
       "1         1.140421e-01  \n",
       "2         7.484658e-02  \n",
       "3         3.385230e-02  \n",
       "4         2.571661e-02  \n",
       "5         2.313153e-02  \n",
       "6         2.048928e-02  \n",
       "7         1.823258e-02  \n",
       "8         1.480994e-02  \n",
       "9         1.352497e-02  \n",
       "10        1.168689e-02  \n",
       "11        1.140109e-02  \n",
       "12        9.519408e-03  \n",
       "13        9.227993e-03  \n",
       "14        9.091511e-03  \n",
       "15        7.878902e-03  \n",
       "16        6.318541e-03  \n",
       "17        6.025353e-03  \n",
       "18        5.975477e-03  \n",
       "19        5.466149e-03  \n",
       "20        5.443841e-03  \n",
       "21        5.302435e-03  \n",
       "22        4.811385e-03  \n",
       "23        4.675333e-03  \n",
       "24        4.491476e-03  \n",
       "25        3.800162e-03  \n",
       "26        3.725324e-03  \n",
       "27        3.536960e-03  \n",
       "28        3.311725e-03  \n",
       "29        2.810217e-03  \n",
       "30        2.677088e-03  \n",
       "31        2.511523e-03  \n",
       "32        2.474588e-03  \n",
       "33        2.337363e-03  \n",
       "34        2.318013e-03  \n",
       "35        2.238276e-03  \n",
       "36        2.224595e-03  \n",
       "37        2.204195e-03  \n",
       "38        2.104992e-03  \n",
       "39        1.985176e-03  \n",
       "40        1.951346e-03  \n",
       "41        1.815288e-03  \n",
       "42        1.811399e-03  \n",
       "43        1.776940e-03  \n",
       "44        1.761643e-03  \n",
       "45        1.680958e-03  \n",
       "46        1.437526e-03  \n",
       "47        1.425016e-03  \n",
       "48        1.221419e-03  \n",
       "49        1.195708e-03  \n",
       "50        1.186015e-03  \n",
       "51        1.185055e-03  \n",
       "52        1.173172e-03  \n",
       "53        9.896240e-04  \n",
       "54        9.600036e-04  \n",
       "55        9.262888e-04  \n",
       "56        8.612596e-04  \n",
       "57        7.925098e-04  \n",
       "58        7.746371e-04  \n",
       "59        7.550383e-04  \n",
       "60        7.282108e-04  \n",
       "61        7.221348e-04  \n",
       "62        5.346558e-04  \n",
       "63        4.543948e-04  \n",
       "64        4.028746e-04  \n",
       "65        6.214462e-18  \n",
       "66        0.000000e+00  \n",
       "67        0.000000e+00  \n",
       "68        0.000000e+00  \n",
       "69        0.000000e+00  \n",
       "70        0.000000e+00  \n",
       "71        0.000000e+00  \n",
       "72        0.000000e+00  \n",
       "73        0.000000e+00  \n",
       "74        0.000000e+00  \n",
       "75        0.000000e+00  \n",
       "76        0.000000e+00  \n",
       "77        0.000000e+00  \n",
       "78        0.000000e+00  \n",
       "79        0.000000e+00  \n",
       "80        0.000000e+00  \n",
       "81        0.000000e+00  \n",
       "82        0.000000e+00  \n",
       "83        0.000000e+00  \n",
       "84        0.000000e+00  \n",
       "85        0.000000e+00  \n",
       "86        0.000000e+00  \n",
       "87        0.000000e+00  \n",
       "88        0.000000e+00  \n",
       "89        0.000000e+00  \n",
       "90        0.000000e+00  \n",
       "91        0.000000e+00  \n",
       "92        0.000000e+00  \n",
       "93        0.000000e+00  \n",
       "94        0.000000e+00  \n",
       "95        0.000000e+00  \n",
       "96        0.000000e+00  \n",
       "97        0.000000e+00  \n",
       "98        0.000000e+00  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_feat_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-running **GridSearchCV Decision Tree Model** w/ only those features in the above list that have a value greater than 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_zero_feat_importance = list(grid_feat_perf.features[:66])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features_temp = df_features.loc[:,non_zero_feat_importance]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conduct Train Test split on new feature set\n",
    "X_train_temp, X_test_temp, y_train_temp, y_test_temp = train_test_split(df_features_temp, df_target, \n",
    "                                                                    random_state=1, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    2.3s\n",
      "[Parallel(n_jobs=-1)]: Done 440 tasks      | elapsed:    5.0s\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:   10.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5, 10, 15, 20],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [5, 7, 10]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the instance\n",
    "grid_dt_ftimp = GridSearchCV(clf, dt_params, cv=10, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "# Fit the decision tree classifier to the training data\n",
    "grid_dt_ftimp.fit(X_train_temp, y_train_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 0.719128\n",
      "Best Params: {'criterion': 'entropy', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5}\n"
     ]
    }
   ],
   "source": [
    "# Show the parameters for the best model and the f1 score for the best model\n",
    "print('Best Score: %f' % (grid_dt_ftimp.best_score_))\n",
    "print('Best Params:', grid_dt_ftimp.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for y values in the training set\n",
    "dtc_grid_train_preds_temp = grid_dt_ftimp.best_estimator_.predict(X_train_temp)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "dtc_grid_test_preds_temp = grid_dt_ftimp.best_estimator_.predict(X_test_temp)\n",
    "\n",
    "# Train Metrics\n",
    "dtc_grid_train_accuracy_temp = metrics.accuracy_score(y_train_temp, dtc_grid_train_preds_temp)\n",
    "dtc_grid_train_precision_temp = metrics.precision_score(y_train_temp, dtc_grid_train_preds_temp)\n",
    "dtc_grid_train_recall_temp = metrics.recall_score(y_train_temp, dtc_grid_train_preds_temp)\n",
    "dtc_grid_train_f1_temp = metrics.f1_score(y_train_temp, dtc_grid_train_preds_temp)\n",
    "\n",
    "# Test Metrics\n",
    "dtc_grid_accuracy_temp = metrics.accuracy_score(y_test_temp, dtc_grid_test_preds_temp)\n",
    "dtc_grid_precision_temp = metrics.precision_score(y_test_temp, dtc_grid_test_preds_temp)\n",
    "dtc_grid_recall_temp = metrics.recall_score(y_test_temp, dtc_grid_test_preds_temp)\n",
    "dtc_grid_f1_temp = metrics.f1_score(y_test_temp, dtc_grid_test_preds_temp)\n",
    "\n",
    "\n",
    "performance.append({'model': 'GridSearchCV Decision Tree Model w/ Feature Importance', '# Features': X_train_temp.shape[1], 'Train Accuracy': dtc_grid_train_accuracy_temp,\n",
    "                    'Test Accuracy': dtc_grid_accuracy_temp, 'Train Recall': dtc_grid_train_recall_temp,\n",
    "                    'Test Recall': dtc_grid_recall_temp, 'Train Precision': dtc_grid_train_precision_temp,\n",
    "                    'Test Precision': dtc_grid_precision_temp, 'Train F1': dtc_grid_train_f1_temp,\n",
    "                    'Test F1': dtc_grid_f1_temp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Feature Im...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>0.869811</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.737190</td>\n",
       "      <td>0.787415</td>\n",
       "      <td>0.731148</td>\n",
       "      <td>0.781599</td>\n",
       "      <td>0.734156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               model  # Features  \\\n",
       "0                          Basic Logistic Regression         231   \n",
       "1                   GridSearchCV Logistic Regression         231   \n",
       "2                 Basic Logistic Regression Selected          99   \n",
       "3          Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                          Basic Decision Tree Model         231   \n",
       "5                   GridSearchCV Decision Tree Model         231   \n",
       "6              Basic Decision Tree Selected Features          99   \n",
       "7  GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "8  GridSearchCV Decision Tree Model w/ Feature Im...          66   \n",
       "\n",
       "   Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0        0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1        0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2        0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3        0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4        1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5        0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6        1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7        0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "8        0.895697       0.869811      0.775869     0.737190         0.787415   \n",
       "\n",
       "   Test Precision  Train F1   Test F1  \n",
       "0        0.678873  0.736012  0.733080  \n",
       "1        0.674221  0.730911  0.726163  \n",
       "2        0.680851  0.730984  0.732824  \n",
       "3        0.696165  0.724406  0.735776  \n",
       "4        0.692810  1.000000  0.696795  \n",
       "5        0.745424  0.803183  0.742952  \n",
       "6        0.660743  1.000000  0.668301  \n",
       "7        0.727124  0.780190  0.731306  \n",
       "8        0.731148  0.781599  0.734156  "
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model slightly improved performance with respect to the model that included more features, so let's narrow down these features even more. The above model ran 65 features that were deemed important by the model, but let's see how the model performs when selecting the top 15 features and combining these with the most imporatnce features from RFECV for our logistic regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_cv_top15 = list(grid_feat_perf.features[0:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['searched',\n",
       " 'contrabn',\n",
       " 'descr_CRIMINAL TRESPASS',\n",
       " 'pf_hcuff',\n",
       " 'sb_hdobj',\n",
       " 'knifcuti',\n",
       " 'age',\n",
       " 'pistol',\n",
       " 'pct_106',\n",
       " 'weight',\n",
       " 'descr_THEFT OF SERVICES',\n",
       " 'descr_CPW',\n",
       " 'othrweap',\n",
       " 'trhsloc_Neither',\n",
       " 'descr_PETIT LARCENY']"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_cv_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a running GridSearch for our decision tree model, the above features have come out and the top 15 most important features in predictions. These 15 features are not the only features included in the decision tree model, however, they were chosen fo rthe sake of comparing feature importance to the RFECV logistic regression model and selecting features consistent in both models for interactions. \n",
    "- **searched**: The fact that an individual was searched\n",
    "- **contrbn**: Some form of contaband was found on the suspect (this could include a weapon, drugs, etc).\n",
    "- **descr_CRIMINAL TRESPASS**: Individuals found trespassing were more likely to be arrested.\n",
    "- **pf_hcuff**: The fact that an individual was placed in handcuffs makes it more likely that they would be arrested. It should be noted there were also individuals who were placed in handcuffs who were not ultimately arrested.\n",
    "- **sb_hdobj**: Basis of search - hard object (likely found during frisk).\n",
    "- **knifcuti**: Individuals with knives found in their possession were more likely to be arrested.\n",
    "- **age**: Again, age plays a large role as mostly younger people are arrested.\n",
    "- **pistol**: pistol found in the suspect's possession is more likely to result in an arrest.\n",
    "- **pct_106**: This precinct likely arrests individuals at higher rates than others likely to dur to location and crime rate in that area. \n",
    "- **weight**: An individual's weight is a likely contributor to likelihood of arrest. This could be due to the correlation between weight and age. \n",
    "- **descr_THEFT OF SERVICES**: When an individual obtains services without compensating the provider.\n",
    "- **descr_CPW**: Criminal possession of a weapon\n",
    "- **othrweap**: Suspect is in possession of another weapon other than those listed. \n",
    "- **trhsloc**: Location of arrest (options are Transit, Public Housing, Neither). Neither is more likely to result in arrest (likely because most crimes are committed in the street). \n",
    "- **descr_PETIT LARCENY**: theft of property less than $1,000.\n",
    "\n",
    "\n",
    "These features will be combined with the best features from logistic regression RFECV to create additional models and feature interactions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finalizing Feature Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age']\n",
      "['descr_CRIMINAL TRESPASS', 'ac_time', 'pct_7', 'weight', 'pct_61', 'cs_casng', 'searched', 'sb_other', 'descr_THEFT OF SERVICES', 'ac_inves', 'pct_14', 'pct_26', 'pf_drwep', 'contrabn', 'sb_admis', 'trhsloc_Neither', 'descr_PETIT LARCENY', 'pistol', 'pct_13', 'pct_52', 'sb_outln', 'knifcuti', 'pct_106', 'othrweap', 'pf_hcuff', 'age', 'pct_25', 'descr_CPW', 'sb_hdobj']\n"
     ]
    }
   ],
   "source": [
    "# Combining these features with those previosuly shown to be most important through recursive feature elimination,\n",
    "# we get the following list of features. I've used the set function to remove duplicates. The only duplicate\n",
    "# feature was age meaning that this was the most robust feature in the dataset. \n",
    "duplicates = []\n",
    "for each in rfecv_feats:\n",
    "    if each in grid_cv_top15:\n",
    "        duplicates.append(each)\n",
    "    else:\n",
    "        continue\n",
    "print(duplicates)\n",
    "\n",
    "important_features = list(set(rfecv_feats + grid_cv_top15))\n",
    "print(important_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, the best performing models with fewer features (for interpretability) are Basic LogReg w/ Selected Features & RFECV and GridSearchCV Decision Tree Model w/ Feature Importance. I'll work with these two when paring down the feature set and creating interaction features.\n",
    "\n",
    "I'll first start by running each of these models with just the final feature set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_features = df_features.loc[:,important_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conduct Train Test split on new feature set\n",
    "X_train_fin, X_test_fin, y_train_fin, y_test_fin = train_test_split(final_features, df_target, \n",
    "                                                                    random_state=1, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic LogReg w/ Final Features & RFECV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=10,\n",
       "      estimator=LogisticRegression(class_weight='balanced', max_iter=1000))"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = RFECV(LogisticRegression(class_weight='balanced', max_iter=1000), step=1, cv=10)\n",
    "selector.fit(X_train_fin, y_train_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(class_weight='balanced', max_iter=1000)\n",
      "14\n",
      "[ 1 13 11 15  6  7  1  1  1 12  9  3 10  1  2  4  1  1  1  5  8  1  1  1\n",
      "  1 16 14  1  1]\n",
      "[ True False False False False False  True  True  True False False False\n",
      " False  True False False  True  True  True False False  True  True  True\n",
      "  True False False  True  True]\n"
     ]
    }
   ],
   "source": [
    "print(selector.estimator_)\n",
    "print(selector.n_features_)\n",
    "print(selector.ranking_)\n",
    "print(selector.support_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['descr_CRIMINAL TRESPASS',\n",
       " 'searched',\n",
       " 'sb_other',\n",
       " 'descr_THEFT OF SERVICES',\n",
       " 'contrabn',\n",
       " 'descr_PETIT LARCENY',\n",
       " 'pistol',\n",
       " 'pct_13',\n",
       " 'knifcuti',\n",
       " 'pct_106',\n",
       " 'othrweap',\n",
       " 'pf_hcuff',\n",
       " 'descr_CPW',\n",
       " 'sb_hdobj']"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a list of selected feature indices. In the list above, True denotes a feature that was selected to \n",
    "# bw included in the model. I'll add each of these indices to a list to later parse out and explore. \n",
    "index_sel_fin = []\n",
    "for i in range(len(selector.support_)):\n",
    "    if selector.support_[i]==True:\n",
    "        index_sel_fin.append(i)\n",
    "    else:\n",
    "        continue\n",
    "\n",
    "rfecv_feats_fin = list(final_features.iloc[:, index_sel_fin].columns)\n",
    "rfecv_feats_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the fitted scaler, transform the training feature set to values between 0 and 1\n",
    "X_train_rfe_fin = selector.transform(X_train_fin)  \n",
    "\n",
    "# Transform the testing set as well to ensure consistency in predictions across feature sets\n",
    "X_test_rfe_fin = selector.transform(X_test_fin) \n",
    "\n",
    "# Predict on the training set\n",
    "fin_rfe_train_preds = selector.estimator_.predict(X_train_rfe_fin)\n",
    "\n",
    "# Predict on the testing set\n",
    "fin_rfe_test_preds = selector.estimator_.predict(X_test_rfe_fin)\n",
    "\n",
    "# Train Metrics\n",
    "basic_lr_fin_rfe_accuracy = metrics.accuracy_score(y_train_fin, fin_rfe_train_preds)\n",
    "basic_lr_fin_rfe_precision = metrics.precision_score(y_train_fin, fin_rfe_train_preds)\n",
    "basic_lr_fin_rfe_recall = metrics.recall_score(y_train_fin, fin_rfe_train_preds)\n",
    "basic_lr_fin_rfe_f1 = metrics.f1_score(y_train_fin, fin_rfe_train_preds)\n",
    "\n",
    "\n",
    "# Test Metrics\n",
    "basic_lr_fin_rfe_test_accuracy = metrics.accuracy_score(y_test_fin, fin_rfe_test_preds)\n",
    "basic_lr_fin_rfe_test_precision = metrics.precision_score(y_test_fin, fin_rfe_test_preds)\n",
    "basic_lr_fin_rfe_test_recall = metrics.recall_score(y_test_fin, fin_rfe_test_preds)\n",
    "basic_lr_fin_rfe_test_f1 = metrics.f1_score(y_test_fin, fin_rfe_test_preds)\n",
    "\n",
    "performance.append({'model': 'Basic LogReg w/ Final Features & RFECV', '# Features': X_train_fin.shape[1], 'Train Accuracy': basic_lr_fin_rfe_accuracy,\n",
    "                    'Test Accuracy': basic_lr_fin_rfe_test_accuracy, 'Train Recall': basic_lr_fin_rfe_recall,\n",
    "                    'Test Recall': basic_lr_fin_rfe_test_recall, 'Train Precision': basic_lr_fin_rfe_precision,\n",
    "                    'Test Precision': basic_lr_fin_rfe_test_precision, 'Train F1': basic_lr_fin_rfe_f1,\n",
    "                    'Test F1': basic_lr_fin_rfe_test_f1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Feature Im...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>0.869811</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.737190</td>\n",
       "      <td>0.787415</td>\n",
       "      <td>0.731148</td>\n",
       "      <td>0.781599</td>\n",
       "      <td>0.734156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Basic LogReg w/ Final Features &amp; RFECV</td>\n",
       "      <td>29</td>\n",
       "      <td>0.858007</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>0.772937</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.680310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.723671</td>\n",
       "      <td>0.718964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               model  # Features  \\\n",
       "0                          Basic Logistic Regression         231   \n",
       "1                   GridSearchCV Logistic Regression         231   \n",
       "2                 Basic Logistic Regression Selected          99   \n",
       "3          Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                          Basic Decision Tree Model         231   \n",
       "5                   GridSearchCV Decision Tree Model         231   \n",
       "6              Basic Decision Tree Selected Features          99   \n",
       "7  GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "8  GridSearchCV Decision Tree Model w/ Feature Im...          66   \n",
       "9             Basic LogReg w/ Final Features & RFECV          29   \n",
       "\n",
       "   Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0        0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1        0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2        0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3        0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4        1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5        0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6        1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7        0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "8        0.895697       0.869811      0.775869     0.737190         0.787415   \n",
       "9        0.858007       0.851270      0.772937     0.780165         0.680310   \n",
       "\n",
       "   Test Precision  Train F1   Test F1  \n",
       "0        0.678873  0.736012  0.733080  \n",
       "1        0.674221  0.730911  0.726163  \n",
       "2        0.680851  0.730984  0.732824  \n",
       "3        0.696165  0.724406  0.735776  \n",
       "4        0.692810  1.000000  0.696795  \n",
       "5        0.745424  0.803183  0.742952  \n",
       "6        0.660743  1.000000  0.668301  \n",
       "7        0.727124  0.780190  0.731306  \n",
       "8        0.731148  0.781599  0.734156  \n",
       "9        0.666667  0.723671  0.718964  "
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model performed slightly worse than original Basic LogReg model with RFECV (3). I'll try this feature set now with a GridSearchCV Decision Tree Model.\n",
    "\n",
    "### GridSearchCV Decision Tree Model with Final Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=-1)]: Done 404 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:    7.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5, 10, 15, 20],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [5, 7, 10]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the instance\n",
    "grid_dt_fin = GridSearchCV(clf, dt_params, cv=10, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "# Fit the decision tree classifier to the training data\n",
    "grid_dt_fin.fit(X_train_fin, y_train_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for y values in the training set\n",
    "dtc_grid_train_preds_fin = grid_dt_fin.best_estimator_.predict(X_train_fin)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "dtc_grid_test_preds_fin = grid_dt_fin.best_estimator_.predict(X_test_fin)\n",
    "\n",
    "# Train Metrics\n",
    "dtc_grid_train_accuracy_fin = metrics.accuracy_score(y_train_fin, dtc_grid_train_preds_fin)\n",
    "dtc_grid_train_precision_fin = metrics.precision_score(y_train_fin, dtc_grid_train_preds_fin)\n",
    "dtc_grid_train_recall_fin = metrics.recall_score(y_train_fin, dtc_grid_train_preds_fin)\n",
    "dtc_grid_train_f1_fin = metrics.f1_score(y_train_fin, dtc_grid_train_preds_fin)\n",
    "\n",
    "# Test Metrics\n",
    "dtc_grid_accuracy_fin = metrics.accuracy_score(y_test_fin, dtc_grid_test_preds_fin)\n",
    "dtc_grid_precision_fin = metrics.precision_score(y_test_fin, dtc_grid_test_preds_fin)\n",
    "dtc_grid_recall_fin = metrics.recall_score(y_test_fin, dtc_grid_test_preds_fin)\n",
    "dtc_grid_f1_fin = metrics.f1_score(y_test_fin, dtc_grid_test_preds_fin)\n",
    "\n",
    "\n",
    "performance.append({'model': 'GridSearchCV Decision Tree Model w/ Final Features', '# Features': X_train_fin.shape[1], 'Train Accuracy': dtc_grid_train_accuracy_fin,\n",
    "                    'Test Accuracy': dtc_grid_accuracy_fin, 'Train Recall': dtc_grid_train_recall_fin,\n",
    "                    'Test Recall': dtc_grid_recall_fin, 'Train Precision': dtc_grid_train_precision_fin,\n",
    "                    'Test Precision': dtc_grid_precision_fin, 'Train F1': dtc_grid_train_f1_fin,\n",
    "                    'Test F1': dtc_grid_f1_fin})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Feature Im...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>0.869811</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.737190</td>\n",
       "      <td>0.787415</td>\n",
       "      <td>0.731148</td>\n",
       "      <td>0.781599</td>\n",
       "      <td>0.734156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Basic LogReg w/ Final Features &amp; RFECV</td>\n",
       "      <td>29</td>\n",
       "      <td>0.858007</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>0.772937</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.680310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.723671</td>\n",
       "      <td>0.718964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Final Feat...</td>\n",
       "      <td>29</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model  # Features  \\\n",
       "0                           Basic Logistic Regression         231   \n",
       "1                    GridSearchCV Logistic Regression         231   \n",
       "2                  Basic Logistic Regression Selected          99   \n",
       "3           Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                           Basic Decision Tree Model         231   \n",
       "5                    GridSearchCV Decision Tree Model         231   \n",
       "6               Basic Decision Tree Selected Features          99   \n",
       "7   GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "8   GridSearchCV Decision Tree Model w/ Feature Im...          66   \n",
       "9              Basic LogReg w/ Final Features & RFECV          29   \n",
       "10  GridSearchCV Decision Tree Model w/ Final Feat...          29   \n",
       "\n",
       "    Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0         0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1         0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2         0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3         0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4         1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5         0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6         1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7         0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "8         0.895697       0.869811      0.775869     0.737190         0.787415   \n",
       "9         0.858007       0.851270      0.772937     0.780165         0.680310   \n",
       "10        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "\n",
       "    Test Precision  Train F1   Test F1  \n",
       "0         0.678873  0.736012  0.733080  \n",
       "1         0.674221  0.730911  0.726163  \n",
       "2         0.680851  0.730984  0.732824  \n",
       "3         0.696165  0.724406  0.735776  \n",
       "4         0.692810  1.000000  0.696795  \n",
       "5         0.745424  0.803183  0.742952  \n",
       "6         0.660743  1.000000  0.668301  \n",
       "7         0.727124  0.780190  0.731306  \n",
       "8         0.731148  0.781599  0.734156  \n",
       "9         0.666667  0.723671  0.718964  \n",
       "10        0.741408  0.762773  0.745066  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model seems to be the winner so far with the highest F1 score and fit on both the training and test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reviewing feature importance\n",
    "list_of_tuples = list(zip(list(final_features.columns), list(grid_dt_fin.best_estimator_.feature_importances_)))\n",
    "fin_feat_perf = pd.DataFrame(list_of_tuples, columns = ['features', 'feature_importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin_feat_perf = fin_feat_perf.sort_values(by='feature_importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin_feat_perf.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>features</th>\n",
       "      <th>feature_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>searched</td>\n",
       "      <td>0.521073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>contrabn</td>\n",
       "      <td>0.122460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>descr_CRIMINAL TRESPASS</td>\n",
       "      <td>0.079757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>pf_hcuff</td>\n",
       "      <td>0.039706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>age</td>\n",
       "      <td>0.036310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28</td>\n",
       "      <td>sb_hdobj</td>\n",
       "      <td>0.027615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21</td>\n",
       "      <td>knifcuti</td>\n",
       "      <td>0.026604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>weight</td>\n",
       "      <td>0.024788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>pistol</td>\n",
       "      <td>0.019486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22</td>\n",
       "      <td>pct_106</td>\n",
       "      <td>0.017992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>trhsloc_Neither</td>\n",
       "      <td>0.015051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27</td>\n",
       "      <td>descr_CPW</td>\n",
       "      <td>0.014855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>othrweap</td>\n",
       "      <td>0.011812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>descr_PETIT LARCENY</td>\n",
       "      <td>0.010841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>descr_THEFT OF SERVICES</td>\n",
       "      <td>0.009459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>ac_time</td>\n",
       "      <td>0.007471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>cs_casng</td>\n",
       "      <td>0.003375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>sb_other</td>\n",
       "      <td>0.002558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>pf_drwep</td>\n",
       "      <td>0.002460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>ac_inves</td>\n",
       "      <td>0.002368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14</td>\n",
       "      <td>sb_admis</td>\n",
       "      <td>0.001898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19</td>\n",
       "      <td>pct_52</td>\n",
       "      <td>0.001059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>pct_61</td>\n",
       "      <td>0.000542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>pct_13</td>\n",
       "      <td>0.000458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20</td>\n",
       "      <td>sb_outln</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>pct_26</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10</td>\n",
       "      <td>pct_14</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>pct_7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>26</td>\n",
       "      <td>pct_25</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index                 features  feature_importance\n",
       "0       6                 searched            0.521073\n",
       "1      13                 contrabn            0.122460\n",
       "2       0  descr_CRIMINAL TRESPASS            0.079757\n",
       "3      24                 pf_hcuff            0.039706\n",
       "4      25                      age            0.036310\n",
       "5      28                 sb_hdobj            0.027615\n",
       "6      21                 knifcuti            0.026604\n",
       "7       3                   weight            0.024788\n",
       "8      17                   pistol            0.019486\n",
       "9      22                  pct_106            0.017992\n",
       "10     15          trhsloc_Neither            0.015051\n",
       "11     27                descr_CPW            0.014855\n",
       "12     23                 othrweap            0.011812\n",
       "13     16      descr_PETIT LARCENY            0.010841\n",
       "14      8  descr_THEFT OF SERVICES            0.009459\n",
       "15      1                  ac_time            0.007471\n",
       "16      5                 cs_casng            0.003375\n",
       "17      7                 sb_other            0.002558\n",
       "18     12                 pf_drwep            0.002460\n",
       "19      9                 ac_inves            0.002368\n",
       "20     14                 sb_admis            0.001898\n",
       "21     19                   pct_52            0.001059\n",
       "22      4                   pct_61            0.000542\n",
       "23     18                   pct_13            0.000458\n",
       "24     20                 sb_outln            0.000000\n",
       "25     11                   pct_26            0.000000\n",
       "26     10                   pct_14            0.000000\n",
       "27      2                    pct_7            0.000000\n",
       "28     26                   pct_25            0.000000"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin_feat_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-running the model removing non-important features\n",
    "top_feat_importance = list(fin_feat_perf.features[:24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_features_temp = final_features.loc[:,top_feat_importance]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conduct Train Test split on new feature set\n",
    "X_train_temp, X_test_temp, y_train_temp, y_test_temp = train_test_split(final_features_temp, df_target, \n",
    "                                                                    random_state=1, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    1.9s\n",
      "[Parallel(n_jobs=-1)]: Done 440 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:    6.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5, 10, 15, 20],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [5, 7, 10]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the instance\n",
    "fin_grid = GridSearchCV(clf, dt_params, cv=10, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "# Fit the decision tree classifier to the training data\n",
    "fin_grid.fit(X_train_temp, y_train_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for y values in the training set\n",
    "fin_dtgrid_train_preds = fin_grid.best_estimator_.predict(X_train_temp)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "fin_dtgrid_test_preds = fin_grid.best_estimator_.predict(X_test_temp)\n",
    "\n",
    "# Train Metrics\n",
    "fingrid_train_accuracy_temp = metrics.accuracy_score(y_train_temp, fin_dtgrid_train_preds)\n",
    "fingrid_train_precision_temp = metrics.precision_score(y_train_temp, fin_dtgrid_train_preds)\n",
    "fingrid_train_recall_temp = metrics.recall_score(y_train_temp, fin_dtgrid_train_preds)\n",
    "fingrid_train_f1_temp = metrics.f1_score(y_train_temp, fin_dtgrid_train_preds)\n",
    "\n",
    "# Test Metrics\n",
    "fingrid_accuracy_temp = metrics.accuracy_score(y_test_temp, fin_dtgrid_test_preds)\n",
    "fingrid_precision_temp = metrics.precision_score(y_test_temp, fin_dtgrid_test_preds)\n",
    "fingrid_recall_temp = metrics.recall_score(y_test_temp, fin_dtgrid_test_preds)\n",
    "fingrid_f1_temp = metrics.f1_score(y_test_temp, fin_dtgrid_test_preds)\n",
    "\n",
    "\n",
    "performance.append({'model': 'Final GridSearchCV Decision Tree Model w/ Feature Importance', '# Features': X_train_temp.shape[1], 'Train Accuracy': fingrid_train_accuracy_temp,\n",
    "                    'Test Accuracy': fingrid_accuracy_temp, 'Train Recall': fingrid_train_recall_temp,\n",
    "                    'Test Recall': fingrid_recall_temp, 'Train Precision': fingrid_train_precision_temp,\n",
    "                    'Test Precision': fingrid_precision_temp, 'Train F1': fingrid_train_f1_temp,\n",
    "                    'Test F1': fingrid_f1_temp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Feature Im...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>0.869811</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.737190</td>\n",
       "      <td>0.787415</td>\n",
       "      <td>0.731148</td>\n",
       "      <td>0.781599</td>\n",
       "      <td>0.734156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Basic LogReg w/ Final Features &amp; RFECV</td>\n",
       "      <td>29</td>\n",
       "      <td>0.858007</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>0.772937</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.680310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.723671</td>\n",
       "      <td>0.718964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Final Feat...</td>\n",
       "      <td>29</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Final GridSearchCV Decision Tree Model w/ Feat...</td>\n",
       "      <td>24</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model  # Features  \\\n",
       "0                           Basic Logistic Regression         231   \n",
       "1                    GridSearchCV Logistic Regression         231   \n",
       "2                  Basic Logistic Regression Selected          99   \n",
       "3           Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                           Basic Decision Tree Model         231   \n",
       "5                    GridSearchCV Decision Tree Model         231   \n",
       "6               Basic Decision Tree Selected Features          99   \n",
       "7   GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "8   GridSearchCV Decision Tree Model w/ Feature Im...          66   \n",
       "9              Basic LogReg w/ Final Features & RFECV          29   \n",
       "10  GridSearchCV Decision Tree Model w/ Final Feat...          29   \n",
       "11  Final GridSearchCV Decision Tree Model w/ Feat...          24   \n",
       "\n",
       "    Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0         0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1         0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2         0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3         0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4         1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5         0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6         1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7         0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "8         0.895697       0.869811      0.775869     0.737190         0.787415   \n",
       "9         0.858007       0.851270      0.772937     0.780165         0.680310   \n",
       "10        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "11        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "\n",
       "    Test Precision  Train F1   Test F1  \n",
       "0         0.678873  0.736012  0.733080  \n",
       "1         0.674221  0.730911  0.726163  \n",
       "2         0.680851  0.730984  0.732824  \n",
       "3         0.696165  0.724406  0.735776  \n",
       "4         0.692810  1.000000  0.696795  \n",
       "5         0.745424  0.803183  0.742952  \n",
       "6         0.660743  1.000000  0.668301  \n",
       "7         0.727124  0.780190  0.731306  \n",
       "8         0.731148  0.781599  0.734156  \n",
       "9         0.666667  0.723671  0.718964  \n",
       "10        0.741408  0.762773  0.745066  \n",
       "11        0.741408  0.762773  0.745066  "
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model performed just as well as the previous model with fewer features, so 11 is the winner as of now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Poly Feature Set with Most Important Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>searched</th>\n",
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       "      <th>descr_CRIMINAL TRESPASS</th>\n",
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       "      <th>pct_52</th>\n",
       "      <th>pct_61</th>\n",
       "      <th>pct_13</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   searched  contrabn  descr_CRIMINAL TRESPASS  pf_hcuff  age  sb_hdobj  \\\n",
       "0         1         0                        0         1   48         0   \n",
       "1         1         0                        0         0   20         0   \n",
       "2         1         0                        0         0   34         0   \n",
       "3         0         0                        0         1   33         0   \n",
       "4         0         0                        0         0   22         0   \n",
       "\n",
       "   knifcuti  weight  pistol  pct_106  trhsloc_Neither  descr_CPW  othrweap  \\\n",
       "0         0     180       0        0                1          0         0   \n",
       "1         0     150       0        0                1          0         0   \n",
       "2         0     140       0        0                1          0         0   \n",
       "3         0     180       0        0                0          1         0   \n",
       "4         0     160       0        0                1          0         0   \n",
       "\n",
       "   descr_PETIT LARCENY  descr_THEFT OF SERVICES  ac_time  cs_casng  sb_other  \\\n",
       "0                    0                        0        1         1         1   \n",
       "1                    0                        0        1         0         1   \n",
       "2                    0                        0        0         0         1   \n",
       "3                    0                        0        0         0         0   \n",
       "4                    0                        0        0         0         0   \n",
       "\n",
       "   pf_drwep  ac_inves  sb_admis  pct_52  pct_61  pct_13  \n",
       "0         0         0         0       0       0       0  \n",
       "1         0         0         0       0       0       0  \n",
       "2         0         0         0       0       0       0  \n",
       "3         0         0         0       0       0       0  \n",
       "4         0         0         0       0       0       0  "
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_features_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>descr_CRIMINAL TRESPASS</th>\n",
       "      <th>ac_time</th>\n",
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       "      <th>descr_CRIMINAL TRESPASS pf_hcuff</th>\n",
       "      <th>descr_CRIMINAL TRESPASS age</th>\n",
       "      <th>descr_CRIMINAL TRESPASS pct_25</th>\n",
       "      <th>descr_CRIMINAL TRESPASS descr_CPW</th>\n",
       "      <th>descr_CRIMINAL TRESPASS sb_hdobj</th>\n",
       "      <th>ac_time^2</th>\n",
       "      <th>ac_time pct_7</th>\n",
       "      <th>ac_time weight</th>\n",
       "      <th>ac_time pct_61</th>\n",
       "      <th>ac_time cs_casng</th>\n",
       "      <th>ac_time searched</th>\n",
       "      <th>ac_time sb_other</th>\n",
       "      <th>ac_time descr_THEFT OF SERVICES</th>\n",
       "      <th>ac_time ac_inves</th>\n",
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       "      <th>ac_time sb_outln</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 464 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   descr_CRIMINAL TRESPASS  ac_time  pct_7  weight  pct_61  cs_casng  \\\n",
       "0                      0.0      1.0    0.0   180.0     0.0       1.0   \n",
       "1                      0.0      1.0    0.0   150.0     0.0       0.0   \n",
       "2                      0.0      0.0    0.0   140.0     0.0       0.0   \n",
       "3                      0.0      0.0    0.0   180.0     0.0       0.0   \n",
       "4                      0.0      0.0    0.0   160.0     0.0       0.0   \n",
       "\n",
       "   searched  sb_other  descr_THEFT OF SERVICES  ac_inves  pct_14  pct_26  \\\n",
       "0       1.0       1.0                      0.0       0.0     0.0     0.0   \n",
       "1       1.0       1.0                      0.0       0.0     0.0     0.0   \n",
       "2       1.0       1.0                      0.0       0.0     0.0     0.0   \n",
       "3       0.0       0.0                      0.0       0.0     0.0     0.0   \n",
       "4       0.0       0.0                      0.0       0.0     0.0     0.0   \n",
       "\n",
       "   pf_drwep  contrabn  sb_admis  trhsloc_Neither  descr_PETIT LARCENY  pistol  \\\n",
       "0       0.0       0.0       0.0              1.0                  0.0     0.0   \n",
       "1       0.0       0.0       0.0              1.0                  0.0     0.0   \n",
       "2       0.0       0.0       0.0              1.0                  0.0     0.0   \n",
       "3       0.0       0.0       0.0              0.0                  0.0     0.0   \n",
       "4       0.0       0.0       0.0              1.0                  0.0     0.0   \n",
       "\n",
       "   pct_13  pct_52  sb_outln  knifcuti  pct_106  othrweap  pf_hcuff   age  \\\n",
       "0     0.0     0.0       0.0       0.0      0.0       0.0       1.0  48.0   \n",
       "1     0.0     0.0       0.0       0.0      0.0       0.0       0.0  20.0   \n",
       "2     0.0     0.0       0.0       0.0      0.0       0.0       0.0  34.0   \n",
       "3     0.0     0.0       0.0       0.0      0.0       0.0       1.0  33.0   \n",
       "4     0.0     0.0       0.0       0.0      0.0       0.0       0.0  22.0   \n",
       "\n",
       "   pct_25  descr_CPW  sb_hdobj  descr_CRIMINAL TRESPASS^2  \\\n",
       "0     0.0        0.0       0.0                        0.0   \n",
       "1     0.0        0.0       0.0                        0.0   \n",
       "2     0.0        0.0       0.0                        0.0   \n",
       "3     0.0        1.0       0.0                        0.0   \n",
       "4     0.0        0.0       0.0                        0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS ac_time  descr_CRIMINAL TRESPASS pct_7  \\\n",
       "0                              0.0                            0.0   \n",
       "1                              0.0                            0.0   \n",
       "2                              0.0                            0.0   \n",
       "3                              0.0                            0.0   \n",
       "4                              0.0                            0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS weight  descr_CRIMINAL TRESPASS pct_61  \\\n",
       "0                             0.0                             0.0   \n",
       "1                             0.0                             0.0   \n",
       "2                             0.0                             0.0   \n",
       "3                             0.0                             0.0   \n",
       "4                             0.0                             0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS cs_casng  descr_CRIMINAL TRESPASS searched  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS sb_other  \\\n",
       "0                               0.0   \n",
       "1                               0.0   \n",
       "2                               0.0   \n",
       "3                               0.0   \n",
       "4                               0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS descr_THEFT OF SERVICES  \\\n",
       "0                                              0.0   \n",
       "1                                              0.0   \n",
       "2                                              0.0   \n",
       "3                                              0.0   \n",
       "4                                              0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS ac_inves  descr_CRIMINAL TRESPASS pct_14  \\\n",
       "0                               0.0                             0.0   \n",
       "1                               0.0                             0.0   \n",
       "2                               0.0                             0.0   \n",
       "3                               0.0                             0.0   \n",
       "4                               0.0                             0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS pct_26  descr_CRIMINAL TRESPASS pf_drwep  \\\n",
       "0                             0.0                               0.0   \n",
       "1                             0.0                               0.0   \n",
       "2                             0.0                               0.0   \n",
       "3                             0.0                               0.0   \n",
       "4                             0.0                               0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS contrabn  descr_CRIMINAL TRESPASS sb_admis  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS trhsloc_Neither  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS descr_PETIT LARCENY  \\\n",
       "0                                          0.0   \n",
       "1                                          0.0   \n",
       "2                                          0.0   \n",
       "3                                          0.0   \n",
       "4                                          0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS pistol  descr_CRIMINAL TRESPASS pct_13  \\\n",
       "0                             0.0                             0.0   \n",
       "1                             0.0                             0.0   \n",
       "2                             0.0                             0.0   \n",
       "3                             0.0                             0.0   \n",
       "4                             0.0                             0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS pct_52  descr_CRIMINAL TRESPASS sb_outln  \\\n",
       "0                             0.0                               0.0   \n",
       "1                             0.0                               0.0   \n",
       "2                             0.0                               0.0   \n",
       "3                             0.0                               0.0   \n",
       "4                             0.0                               0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS knifcuti  descr_CRIMINAL TRESPASS pct_106  \\\n",
       "0                               0.0                              0.0   \n",
       "1                               0.0                              0.0   \n",
       "2                               0.0                              0.0   \n",
       "3                               0.0                              0.0   \n",
       "4                               0.0                              0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS othrweap  descr_CRIMINAL TRESPASS pf_hcuff  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS age  descr_CRIMINAL TRESPASS pct_25  \\\n",
       "0                          0.0                             0.0   \n",
       "1                          0.0                             0.0   \n",
       "2                          0.0                             0.0   \n",
       "3                          0.0                             0.0   \n",
       "4                          0.0                             0.0   \n",
       "\n",
       "   descr_CRIMINAL TRESPASS descr_CPW  descr_CRIMINAL TRESPASS sb_hdobj  \\\n",
       "0                                0.0                               0.0   \n",
       "1                                0.0                               0.0   \n",
       "2                                0.0                               0.0   \n",
       "3                                0.0                               0.0   \n",
       "4                                0.0                               0.0   \n",
       "\n",
       "   ac_time^2  ac_time pct_7  ac_time weight  ac_time pct_61  ac_time cs_casng  \\\n",
       "0        1.0            0.0           180.0             0.0               1.0   \n",
       "1        1.0            0.0           150.0             0.0               0.0   \n",
       "2        0.0            0.0             0.0             0.0               0.0   \n",
       "3        0.0            0.0             0.0             0.0               0.0   \n",
       "4        0.0            0.0             0.0             0.0               0.0   \n",
       "\n",
       "   ac_time searched  ac_time sb_other  ac_time descr_THEFT OF SERVICES  \\\n",
       "0               1.0               1.0                              0.0   \n",
       "1               1.0               1.0                              0.0   \n",
       "2               0.0               0.0                              0.0   \n",
       "3               0.0               0.0                              0.0   \n",
       "4               0.0               0.0                              0.0   \n",
       "\n",
       "   ac_time ac_inves  ac_time pct_14  ac_time pct_26  ac_time pf_drwep  \\\n",
       "0               0.0             0.0             0.0               0.0   \n",
       "1               0.0             0.0             0.0               0.0   \n",
       "2               0.0             0.0             0.0               0.0   \n",
       "3               0.0             0.0             0.0               0.0   \n",
       "4               0.0             0.0             0.0               0.0   \n",
       "\n",
       "   ac_time contrabn  ac_time sb_admis  ac_time trhsloc_Neither  \\\n",
       "0               0.0               0.0                      1.0   \n",
       "1               0.0               0.0                      1.0   \n",
       "2               0.0               0.0                      0.0   \n",
       "3               0.0               0.0                      0.0   \n",
       "4               0.0               0.0                      0.0   \n",
       "\n",
       "   ac_time descr_PETIT LARCENY  ac_time pistol  ac_time pct_13  \\\n",
       "0                          0.0             0.0             0.0   \n",
       "1                          0.0             0.0             0.0   \n",
       "2                          0.0             0.0             0.0   \n",
       "3                          0.0             0.0             0.0   \n",
       "4                          0.0             0.0             0.0   \n",
       "\n",
       "   ac_time pct_52  ac_time sb_outln  ac_time knifcuti  ac_time pct_106  \\\n",
       "0             0.0               0.0               0.0              0.0   \n",
       "1             0.0               0.0               0.0              0.0   \n",
       "2             0.0               0.0               0.0              0.0   \n",
       "3             0.0               0.0               0.0              0.0   \n",
       "4             0.0               0.0               0.0              0.0   \n",
       "\n",
       "   ac_time othrweap  ac_time pf_hcuff  ac_time age  ac_time pct_25  \\\n",
       "0               0.0               1.0         48.0             0.0   \n",
       "1               0.0               0.0         20.0             0.0   \n",
       "2               0.0               0.0          0.0             0.0   \n",
       "3               0.0               0.0          0.0             0.0   \n",
       "4               0.0               0.0          0.0             0.0   \n",
       "\n",
       "   ac_time descr_CPW  ac_time sb_hdobj  pct_7^2  pct_7 weight  pct_7 pct_61  \\\n",
       "0                0.0               0.0      0.0           0.0           0.0   \n",
       "1                0.0               0.0      0.0           0.0           0.0   \n",
       "2                0.0               0.0      0.0           0.0           0.0   \n",
       "3                0.0               0.0      0.0           0.0           0.0   \n",
       "4                0.0               0.0      0.0           0.0           0.0   \n",
       "\n",
       "   pct_7 cs_casng  pct_7 searched  pct_7 sb_other  \\\n",
       "0             0.0             0.0             0.0   \n",
       "1             0.0             0.0             0.0   \n",
       "2             0.0             0.0             0.0   \n",
       "3             0.0             0.0             0.0   \n",
       "4             0.0             0.0             0.0   \n",
       "\n",
       "   pct_7 descr_THEFT OF SERVICES  pct_7 ac_inves  pct_7 pct_14  pct_7 pct_26  \\\n",
       "0                            0.0             0.0           0.0           0.0   \n",
       "1                            0.0             0.0           0.0           0.0   \n",
       "2                            0.0             0.0           0.0           0.0   \n",
       "3                            0.0             0.0           0.0           0.0   \n",
       "4                            0.0             0.0           0.0           0.0   \n",
       "\n",
       "   pct_7 pf_drwep  pct_7 contrabn  pct_7 sb_admis  pct_7 trhsloc_Neither  ...  \\\n",
       "0             0.0             0.0             0.0                    0.0  ...   \n",
       "1             0.0             0.0             0.0                    0.0  ...   \n",
       "2             0.0             0.0             0.0                    0.0  ...   \n",
       "3             0.0             0.0             0.0                    0.0  ...   \n",
       "4             0.0             0.0             0.0                    0.0  ...   \n",
       "\n",
       "   trhsloc_Neither sb_outln  trhsloc_Neither knifcuti  \\\n",
       "0                       0.0                       0.0   \n",
       "1                       0.0                       0.0   \n",
       "2                       0.0                       0.0   \n",
       "3                       0.0                       0.0   \n",
       "4                       0.0                       0.0   \n",
       "\n",
       "   trhsloc_Neither pct_106  trhsloc_Neither othrweap  \\\n",
       "0                      0.0                       0.0   \n",
       "1                      0.0                       0.0   \n",
       "2                      0.0                       0.0   \n",
       "3                      0.0                       0.0   \n",
       "4                      0.0                       0.0   \n",
       "\n",
       "   trhsloc_Neither pf_hcuff  trhsloc_Neither age  trhsloc_Neither pct_25  \\\n",
       "0                       1.0                 48.0                     0.0   \n",
       "1                       0.0                 20.0                     0.0   \n",
       "2                       0.0                 34.0                     0.0   \n",
       "3                       0.0                  0.0                     0.0   \n",
       "4                       0.0                 22.0                     0.0   \n",
       "\n",
       "   trhsloc_Neither descr_CPW  trhsloc_Neither sb_hdobj  descr_PETIT LARCENY^2  \\\n",
       "0                        0.0                       0.0                    0.0   \n",
       "1                        0.0                       0.0                    0.0   \n",
       "2                        0.0                       0.0                    0.0   \n",
       "3                        0.0                       0.0                    0.0   \n",
       "4                        0.0                       0.0                    0.0   \n",
       "\n",
       "   descr_PETIT LARCENY pistol  descr_PETIT LARCENY pct_13  \\\n",
       "0                         0.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "\n",
       "   descr_PETIT LARCENY pct_52  descr_PETIT LARCENY sb_outln  \\\n",
       "0                         0.0                           0.0   \n",
       "1                         0.0                           0.0   \n",
       "2                         0.0                           0.0   \n",
       "3                         0.0                           0.0   \n",
       "4                         0.0                           0.0   \n",
       "\n",
       "   descr_PETIT LARCENY knifcuti  descr_PETIT LARCENY pct_106  \\\n",
       "0                           0.0                          0.0   \n",
       "1                           0.0                          0.0   \n",
       "2                           0.0                          0.0   \n",
       "3                           0.0                          0.0   \n",
       "4                           0.0                          0.0   \n",
       "\n",
       "   descr_PETIT LARCENY othrweap  descr_PETIT LARCENY pf_hcuff  \\\n",
       "0                           0.0                           0.0   \n",
       "1                           0.0                           0.0   \n",
       "2                           0.0                           0.0   \n",
       "3                           0.0                           0.0   \n",
       "4                           0.0                           0.0   \n",
       "\n",
       "   descr_PETIT LARCENY age  descr_PETIT LARCENY pct_25  \\\n",
       "0                      0.0                         0.0   \n",
       "1                      0.0                         0.0   \n",
       "2                      0.0                         0.0   \n",
       "3                      0.0                         0.0   \n",
       "4                      0.0                         0.0   \n",
       "\n",
       "   descr_PETIT LARCENY descr_CPW  descr_PETIT LARCENY sb_hdobj  pistol^2  \\\n",
       "0                            0.0                           0.0       0.0   \n",
       "1                            0.0                           0.0       0.0   \n",
       "2                            0.0                           0.0       0.0   \n",
       "3                            0.0                           0.0       0.0   \n",
       "4                            0.0                           0.0       0.0   \n",
       "\n",
       "   pistol pct_13  pistol pct_52  pistol sb_outln  pistol knifcuti  \\\n",
       "0            0.0            0.0              0.0              0.0   \n",
       "1            0.0            0.0              0.0              0.0   \n",
       "2            0.0            0.0              0.0              0.0   \n",
       "3            0.0            0.0              0.0              0.0   \n",
       "4            0.0            0.0              0.0              0.0   \n",
       "\n",
       "   pistol pct_106  pistol othrweap  pistol pf_hcuff  pistol age  \\\n",
       "0             0.0              0.0              0.0         0.0   \n",
       "1             0.0              0.0              0.0         0.0   \n",
       "2             0.0              0.0              0.0         0.0   \n",
       "3             0.0              0.0              0.0         0.0   \n",
       "4             0.0              0.0              0.0         0.0   \n",
       "\n",
       "   pistol pct_25  pistol descr_CPW  pistol sb_hdobj  pct_13^2  pct_13 pct_52  \\\n",
       "0            0.0               0.0              0.0       0.0            0.0   \n",
       "1            0.0               0.0              0.0       0.0            0.0   \n",
       "2            0.0               0.0              0.0       0.0            0.0   \n",
       "3            0.0               0.0              0.0       0.0            0.0   \n",
       "4            0.0               0.0              0.0       0.0            0.0   \n",
       "\n",
       "   pct_13 sb_outln  pct_13 knifcuti  pct_13 pct_106  pct_13 othrweap  \\\n",
       "0              0.0              0.0             0.0              0.0   \n",
       "1              0.0              0.0             0.0              0.0   \n",
       "2              0.0              0.0             0.0              0.0   \n",
       "3              0.0              0.0             0.0              0.0   \n",
       "4              0.0              0.0             0.0              0.0   \n",
       "\n",
       "   pct_13 pf_hcuff  pct_13 age  pct_13 pct_25  pct_13 descr_CPW  \\\n",
       "0              0.0         0.0            0.0               0.0   \n",
       "1              0.0         0.0            0.0               0.0   \n",
       "2              0.0         0.0            0.0               0.0   \n",
       "3              0.0         0.0            0.0               0.0   \n",
       "4              0.0         0.0            0.0               0.0   \n",
       "\n",
       "   pct_13 sb_hdobj  pct_52^2  pct_52 sb_outln  pct_52 knifcuti  \\\n",
       "0              0.0       0.0              0.0              0.0   \n",
       "1              0.0       0.0              0.0              0.0   \n",
       "2              0.0       0.0              0.0              0.0   \n",
       "3              0.0       0.0              0.0              0.0   \n",
       "4              0.0       0.0              0.0              0.0   \n",
       "\n",
       "   pct_52 pct_106  pct_52 othrweap  pct_52 pf_hcuff  pct_52 age  \\\n",
       "0             0.0              0.0              0.0         0.0   \n",
       "1             0.0              0.0              0.0         0.0   \n",
       "2             0.0              0.0              0.0         0.0   \n",
       "3             0.0              0.0              0.0         0.0   \n",
       "4             0.0              0.0              0.0         0.0   \n",
       "\n",
       "   pct_52 pct_25  pct_52 descr_CPW  pct_52 sb_hdobj  sb_outln^2  \\\n",
       "0            0.0               0.0              0.0         0.0   \n",
       "1            0.0               0.0              0.0         0.0   \n",
       "2            0.0               0.0              0.0         0.0   \n",
       "3            0.0               0.0              0.0         0.0   \n",
       "4            0.0               0.0              0.0         0.0   \n",
       "\n",
       "   sb_outln knifcuti  sb_outln pct_106  sb_outln othrweap  sb_outln pf_hcuff  \\\n",
       "0                0.0               0.0                0.0                0.0   \n",
       "1                0.0               0.0                0.0                0.0   \n",
       "2                0.0               0.0                0.0                0.0   \n",
       "3                0.0               0.0                0.0                0.0   \n",
       "4                0.0               0.0                0.0                0.0   \n",
       "\n",
       "   sb_outln age  sb_outln pct_25  sb_outln descr_CPW  sb_outln sb_hdobj  \\\n",
       "0           0.0              0.0                 0.0                0.0   \n",
       "1           0.0              0.0                 0.0                0.0   \n",
       "2           0.0              0.0                 0.0                0.0   \n",
       "3           0.0              0.0                 0.0                0.0   \n",
       "4           0.0              0.0                 0.0                0.0   \n",
       "\n",
       "   knifcuti^2  knifcuti pct_106  knifcuti othrweap  knifcuti pf_hcuff  \\\n",
       "0         0.0               0.0                0.0                0.0   \n",
       "1         0.0               0.0                0.0                0.0   \n",
       "2         0.0               0.0                0.0                0.0   \n",
       "3         0.0               0.0                0.0                0.0   \n",
       "4         0.0               0.0                0.0                0.0   \n",
       "\n",
       "   knifcuti age  knifcuti pct_25  knifcuti descr_CPW  knifcuti sb_hdobj  \\\n",
       "0           0.0              0.0                 0.0                0.0   \n",
       "1           0.0              0.0                 0.0                0.0   \n",
       "2           0.0              0.0                 0.0                0.0   \n",
       "3           0.0              0.0                 0.0                0.0   \n",
       "4           0.0              0.0                 0.0                0.0   \n",
       "\n",
       "   pct_106^2  pct_106 othrweap  pct_106 pf_hcuff  pct_106 age  pct_106 pct_25  \\\n",
       "0        0.0               0.0               0.0          0.0             0.0   \n",
       "1        0.0               0.0               0.0          0.0             0.0   \n",
       "2        0.0               0.0               0.0          0.0             0.0   \n",
       "3        0.0               0.0               0.0          0.0             0.0   \n",
       "4        0.0               0.0               0.0          0.0             0.0   \n",
       "\n",
       "   pct_106 descr_CPW  pct_106 sb_hdobj  othrweap^2  othrweap pf_hcuff  \\\n",
       "0                0.0               0.0         0.0                0.0   \n",
       "1                0.0               0.0         0.0                0.0   \n",
       "2                0.0               0.0         0.0                0.0   \n",
       "3                0.0               0.0         0.0                0.0   \n",
       "4                0.0               0.0         0.0                0.0   \n",
       "\n",
       "   othrweap age  othrweap pct_25  othrweap descr_CPW  othrweap sb_hdobj  \\\n",
       "0           0.0              0.0                 0.0                0.0   \n",
       "1           0.0              0.0                 0.0                0.0   \n",
       "2           0.0              0.0                 0.0                0.0   \n",
       "3           0.0              0.0                 0.0                0.0   \n",
       "4           0.0              0.0                 0.0                0.0   \n",
       "\n",
       "   pf_hcuff^2  pf_hcuff age  pf_hcuff pct_25  pf_hcuff descr_CPW  \\\n",
       "0         1.0          48.0              0.0                 0.0   \n",
       "1         0.0           0.0              0.0                 0.0   \n",
       "2         0.0           0.0              0.0                 0.0   \n",
       "3         1.0          33.0              0.0                 1.0   \n",
       "4         0.0           0.0              0.0                 0.0   \n",
       "\n",
       "   pf_hcuff sb_hdobj   age^2  age pct_25  age descr_CPW  age sb_hdobj  \\\n",
       "0                0.0  2304.0         0.0            0.0           0.0   \n",
       "1                0.0   400.0         0.0            0.0           0.0   \n",
       "2                0.0  1156.0         0.0            0.0           0.0   \n",
       "3                0.0  1089.0         0.0           33.0           0.0   \n",
       "4                0.0   484.0         0.0            0.0           0.0   \n",
       "\n",
       "   pct_25^2  pct_25 descr_CPW  pct_25 sb_hdobj  descr_CPW^2  \\\n",
       "0       0.0               0.0              0.0          0.0   \n",
       "1       0.0               0.0              0.0          0.0   \n",
       "2       0.0               0.0              0.0          0.0   \n",
       "3       0.0               0.0              0.0          1.0   \n",
       "4       0.0               0.0              0.0          0.0   \n",
       "\n",
       "   descr_CPW sb_hdobj  sb_hdobj^2  \n",
       "0                 0.0         0.0  \n",
       "1                 0.0         0.0  \n",
       "2                 0.0         0.0  \n",
       "3                 0.0         0.0  \n",
       "4                 0.0         0.0  \n",
       "\n",
       "[5 rows x 464 columns]"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "#Second degree polynomial\n",
    "poly_2 = PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly2_data = poly_2.fit_transform(final_features)\n",
    "poly2_columns = poly_2.get_feature_names(final_features.columns)\n",
    "df_poly2 = pd.DataFrame(poly2_data, columns=poly2_columns)\n",
    "df_poly2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conduct Train Test split on new feature set\n",
    "X_train_poly, X_test_poly, y_train_poly, y_test_poly = train_test_split(df_poly2, df_target, \n",
    "                                                                    random_state=1, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    5.0s\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:   14.0s\n",
      "[Parallel(n_jobs=-1)]: Done 426 tasks      | elapsed:   39.0s\n",
      "[Parallel(n_jobs=-1)]: Done 776 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:  1.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5, 10, 15, 20],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [5, 7, 10]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the instance\n",
    "grid_dt_poly = GridSearchCV(clf, dt_params, cv=10, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "# Fit the decision tree classifier to the training data\n",
    "grid_dt_poly.fit(X_train_poly, y_train_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for y values in the training set\n",
    "dtc_grid_train_preds_poly = grid_dt_poly.best_estimator_.predict(X_train_poly)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "dtc_grid_test_preds_poly = grid_dt_poly.best_estimator_.predict(X_test_poly)\n",
    "\n",
    "# Train Metrics\n",
    "dtc_grid_train_accuracy_poly = metrics.accuracy_score(y_train_poly, dtc_grid_train_preds_poly)\n",
    "dtc_grid_train_precision_poly = metrics.precision_score(y_train_poly, dtc_grid_train_preds_poly)\n",
    "dtc_grid_train_recall_poly = metrics.recall_score(y_train_poly, dtc_grid_train_preds_poly)\n",
    "dtc_grid_train_f1_poly = metrics.f1_score(y_train_poly, dtc_grid_train_preds_poly)\n",
    "\n",
    "# Test Metrics\n",
    "dtc_grid_accuracy_poly = metrics.accuracy_score(y_test_poly, dtc_grid_test_preds_poly)\n",
    "dtc_grid_precision_poly = metrics.precision_score(y_test_poly, dtc_grid_test_preds_poly)\n",
    "dtc_grid_recall_poly = metrics.recall_score(y_test_poly, dtc_grid_test_preds_poly)\n",
    "dtc_grid_f1_poly = metrics.f1_score(y_test_poly, dtc_grid_test_preds_poly)\n",
    "\n",
    "\n",
    "performance.append({'model': 'GridSearchCV Decision Tree Model w/ Poly Features', '# Features': X_train_poly.shape[1], 'Train Accuracy': dtc_grid_train_accuracy_poly,\n",
    "                    'Test Accuracy': dtc_grid_accuracy_poly, 'Train Recall': dtc_grid_train_recall_poly,\n",
    "                    'Test Recall': dtc_grid_recall_poly, 'Train Precision': dtc_grid_train_precision_poly,\n",
    "                    'Test Precision': dtc_grid_precision_poly, 'Train F1': dtc_grid_train_f1_poly,\n",
    "                    'Test F1': dtc_grid_f1_poly})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Feature Im...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>0.869811</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.737190</td>\n",
       "      <td>0.787415</td>\n",
       "      <td>0.731148</td>\n",
       "      <td>0.781599</td>\n",
       "      <td>0.734156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Basic LogReg w/ Final Features &amp; RFECV</td>\n",
       "      <td>29</td>\n",
       "      <td>0.858007</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>0.772937</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.680310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.723671</td>\n",
       "      <td>0.718964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Final Feat...</td>\n",
       "      <td>29</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Final GridSearchCV Decision Tree Model w/ Feat...</td>\n",
       "      <td>24</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Poly Features</td>\n",
       "      <td>464</td>\n",
       "      <td>0.887937</td>\n",
       "      <td>0.865377</td>\n",
       "      <td>0.782572</td>\n",
       "      <td>0.752066</td>\n",
       "      <td>0.759041</td>\n",
       "      <td>0.712050</td>\n",
       "      <td>0.770627</td>\n",
       "      <td>0.731511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model  # Features  \\\n",
       "0                           Basic Logistic Regression         231   \n",
       "1                    GridSearchCV Logistic Regression         231   \n",
       "2                  Basic Logistic Regression Selected          99   \n",
       "3           Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                           Basic Decision Tree Model         231   \n",
       "5                    GridSearchCV Decision Tree Model         231   \n",
       "6               Basic Decision Tree Selected Features          99   \n",
       "7   GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "8   GridSearchCV Decision Tree Model w/ Feature Im...          66   \n",
       "9              Basic LogReg w/ Final Features & RFECV          29   \n",
       "10  GridSearchCV Decision Tree Model w/ Final Feat...          29   \n",
       "11  Final GridSearchCV Decision Tree Model w/ Feat...          24   \n",
       "12  GridSearchCV Decision Tree Model w/ Poly Features         464   \n",
       "\n",
       "    Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0         0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1         0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2         0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3         0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4         1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5         0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6         1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7         0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "8         0.895697       0.869811      0.775869     0.737190         0.787415   \n",
       "9         0.858007       0.851270      0.772937     0.780165         0.680310   \n",
       "10        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "11        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "12        0.887937       0.865377      0.782572     0.752066         0.759041   \n",
       "\n",
       "    Test Precision  Train F1   Test F1  \n",
       "0         0.678873  0.736012  0.733080  \n",
       "1         0.674221  0.730911  0.726163  \n",
       "2         0.680851  0.730984  0.732824  \n",
       "3         0.696165  0.724406  0.735776  \n",
       "4         0.692810  1.000000  0.696795  \n",
       "5         0.745424  0.803183  0.742952  \n",
       "6         0.660743  1.000000  0.668301  \n",
       "7         0.727124  0.780190  0.731306  \n",
       "8         0.731148  0.781599  0.734156  \n",
       "9         0.666667  0.723671  0.718964  \n",
       "10        0.741408  0.762773  0.745066  \n",
       "11        0.741408  0.762773  0.745066  \n",
       "12        0.712050  0.770627  0.731511  "
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reviewing feature importance\n",
    "list_of_tuples = list(zip(list(df_poly2.columns), list(grid_dt_poly.best_estimator_.feature_importances_)))\n",
    "poly_feat_perf = pd.DataFrame(list_of_tuples, columns = ['features', 'feature_importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_feat_perf = poly_feat_perf.sort_values(by='feature_importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_feat_perf.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>features</th>\n",
       "      <th>feature_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>weight searched</td>\n",
       "      <td>5.821202e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>340</td>\n",
       "      <td>contrabn age</td>\n",
       "      <td>9.320754e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>descr_CRIMINAL TRESPASS age</td>\n",
       "      <td>6.870072e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>450</td>\n",
       "      <td>pf_hcuff age</td>\n",
       "      <td>3.613620e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>contrabn</td>\n",
       "      <td>2.124799e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>457</td>\n",
       "      <td>age sb_hdobj</td>\n",
       "      <td>1.739054e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>125</td>\n",
       "      <td>weight trhsloc_Neither</td>\n",
       "      <td>1.362526e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>432</td>\n",
       "      <td>knifcuti age</td>\n",
       "      <td>1.174297e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>135</td>\n",
       "      <td>weight age</td>\n",
       "      <td>1.167958e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>118</td>\n",
       "      <td>weight descr_THEFT OF SERVICES</td>\n",
       "      <td>1.039716e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>382</td>\n",
       "      <td>descr_PETIT LARCENY age</td>\n",
       "      <td>1.019650e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>137</td>\n",
       "      <td>weight descr_CPW</td>\n",
       "      <td>8.655833e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>386</td>\n",
       "      <td>pistol^2</td>\n",
       "      <td>7.507264e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>445</td>\n",
       "      <td>othrweap age</td>\n",
       "      <td>6.878391e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>127</td>\n",
       "      <td>weight pistol</td>\n",
       "      <td>6.732962e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>407</td>\n",
       "      <td>pct_13 descr_CPW</td>\n",
       "      <td>6.383574e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>knifcuti</td>\n",
       "      <td>6.288697e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>369</td>\n",
       "      <td>trhsloc_Neither age</td>\n",
       "      <td>6.253864e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36</td>\n",
       "      <td>descr_CRIMINAL TRESPASS sb_other</td>\n",
       "      <td>6.200304e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>367</td>\n",
       "      <td>trhsloc_Neither othrweap</td>\n",
       "      <td>4.280606e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>207</td>\n",
       "      <td>searched age</td>\n",
       "      <td>3.840215e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>131</td>\n",
       "      <td>weight knifcuti</td>\n",
       "      <td>3.779363e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>ac_time cs_casng</td>\n",
       "      <td>3.492739e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>134</td>\n",
       "      <td>weight pf_hcuff</td>\n",
       "      <td>3.073215e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>82</td>\n",
       "      <td>ac_time age</td>\n",
       "      <td>3.069850e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>231</td>\n",
       "      <td>sb_other descr_CPW</td>\n",
       "      <td>2.699331e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>439</td>\n",
       "      <td>pct_106 age</td>\n",
       "      <td>2.674691e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>442</td>\n",
       "      <td>pct_106 sb_hdobj</td>\n",
       "      <td>2.431249e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12</td>\n",
       "      <td>pf_drwep</td>\n",
       "      <td>2.399020e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>133</td>\n",
       "      <td>weight othrweap</td>\n",
       "      <td>2.277867e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>119</td>\n",
       "      <td>weight ac_inves</td>\n",
       "      <td>2.254475e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>72</td>\n",
       "      <td>ac_time trhsloc_Neither</td>\n",
       "      <td>2.208082e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>456</td>\n",
       "      <td>age descr_CPW</td>\n",
       "      <td>2.057474e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>454</td>\n",
       "      <td>age^2</td>\n",
       "      <td>2.051966e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17</td>\n",
       "      <td>pistol</td>\n",
       "      <td>1.998359e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>434</td>\n",
       "      <td>knifcuti descr_CPW</td>\n",
       "      <td>1.704015e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>143</td>\n",
       "      <td>pct_61 descr_THEFT OF SERVICES</td>\n",
       "      <td>1.589204e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>359</td>\n",
       "      <td>trhsloc_Neither^2</td>\n",
       "      <td>1.487767e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>270</td>\n",
       "      <td>ac_inves age</td>\n",
       "      <td>1.432151e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>25</td>\n",
       "      <td>age</td>\n",
       "      <td>1.354816e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>22</td>\n",
       "      <td>pct_106</td>\n",
       "      <td>1.322338e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>366</td>\n",
       "      <td>trhsloc_Neither pct_106</td>\n",
       "      <td>1.226287e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>371</td>\n",
       "      <td>trhsloc_Neither descr_CPW</td>\n",
       "      <td>1.195779e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>81</td>\n",
       "      <td>ac_time pf_hcuff</td>\n",
       "      <td>1.146482e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>227</td>\n",
       "      <td>sb_other othrweap</td>\n",
       "      <td>9.724865e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>394</td>\n",
       "      <td>pistol age</td>\n",
       "      <td>9.705527e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>66</td>\n",
       "      <td>ac_time ac_inves</td>\n",
       "      <td>8.914185e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>415</td>\n",
       "      <td>pct_52 age</td>\n",
       "      <td>8.600217e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>115</td>\n",
       "      <td>weight cs_casng</td>\n",
       "      <td>8.313373e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>184</td>\n",
       "      <td>cs_casng age</td>\n",
       "      <td>8.292099e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>77</td>\n",
       "      <td>ac_time sb_outln</td>\n",
       "      <td>8.239237e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>272</td>\n",
       "      <td>ac_inves descr_CPW</td>\n",
       "      <td>8.163509e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>243</td>\n",
       "      <td>descr_THEFT OF SERVICES pct_13</td>\n",
       "      <td>7.303412e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>31</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_7</td>\n",
       "      <td>6.763673e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>32</td>\n",
       "      <td>descr_CRIMINAL TRESPASS weight</td>\n",
       "      <td>6.702216e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>113</td>\n",
       "      <td>weight^2</td>\n",
       "      <td>4.109354e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>355</td>\n",
       "      <td>sb_admis age</td>\n",
       "      <td>3.642521e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>334</td>\n",
       "      <td>contrabn pct_52</td>\n",
       "      <td>2.752272e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>229</td>\n",
       "      <td>sb_other age</td>\n",
       "      <td>2.729807e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>24</td>\n",
       "      <td>pf_hcuff</td>\n",
       "      <td>2.629834e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>405</td>\n",
       "      <td>pct_13 age</td>\n",
       "      <td>2.252013e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>117</td>\n",
       "      <td>weight sb_other</td>\n",
       "      <td>1.772595e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "      <td>ac_time</td>\n",
       "      <td>1.588139e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>423</td>\n",
       "      <td>sb_outln pf_hcuff</td>\n",
       "      <td>1.052866e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>353</td>\n",
       "      <td>sb_admis othrweap</td>\n",
       "      <td>1.032288e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>230</td>\n",
       "      <td>sb_other pct_25</td>\n",
       "      <td>9.125261e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>132</td>\n",
       "      <td>weight pct_106</td>\n",
       "      <td>4.701608e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>176</td>\n",
       "      <td>cs_casng pistol</td>\n",
       "      <td>4.045315e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>58</td>\n",
       "      <td>ac_time^2</td>\n",
       "      <td>4.013621e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>descr_CRIMINAL TRESPASS</td>\n",
       "      <td>2.027425e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>85</td>\n",
       "      <td>ac_time sb_hdobj</td>\n",
       "      <td>1.858473e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>293</td>\n",
       "      <td>pct_26^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>264</td>\n",
       "      <td>ac_inves pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>266</td>\n",
       "      <td>ac_inves knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>267</td>\n",
       "      <td>ac_inves pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>328</td>\n",
       "      <td>contrabn^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>327</td>\n",
       "      <td>pf_drwep sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>326</td>\n",
       "      <td>pf_drwep descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>268</td>\n",
       "      <td>ac_inves othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>325</td>\n",
       "      <td>pf_drwep pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>269</td>\n",
       "      <td>ac_inves pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>324</td>\n",
       "      <td>pf_drwep age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>271</td>\n",
       "      <td>ac_inves pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>273</td>\n",
       "      <td>ac_inves sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>323</td>\n",
       "      <td>pf_drwep pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>274</td>\n",
       "      <td>pct_14^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>275</td>\n",
       "      <td>pct_14 pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>276</td>\n",
       "      <td>pct_14 pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>277</td>\n",
       "      <td>pct_14 contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>278</td>\n",
       "      <td>pct_14 sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>279</td>\n",
       "      <td>pct_14 trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>265</td>\n",
       "      <td>ac_inves sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>263</td>\n",
       "      <td>ac_inves pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>281</td>\n",
       "      <td>pct_14 pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>329</td>\n",
       "      <td>contrabn sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>341</td>\n",
       "      <td>contrabn pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>256</td>\n",
       "      <td>ac_inves pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>339</td>\n",
       "      <td>contrabn pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>338</td>\n",
       "      <td>contrabn othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>337</td>\n",
       "      <td>contrabn pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>336</td>\n",
       "      <td>contrabn knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>335</td>\n",
       "      <td>contrabn sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>257</td>\n",
       "      <td>ac_inves pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>258</td>\n",
       "      <td>ac_inves contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>333</td>\n",
       "      <td>contrabn pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>259</td>\n",
       "      <td>ac_inves sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>260</td>\n",
       "      <td>ac_inves trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>332</td>\n",
       "      <td>contrabn pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>331</td>\n",
       "      <td>contrabn descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>330</td>\n",
       "      <td>contrabn trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>261</td>\n",
       "      <td>ac_inves descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>262</td>\n",
       "      <td>ac_inves pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>280</td>\n",
       "      <td>pct_14 descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>282</td>\n",
       "      <td>pct_14 pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>292</td>\n",
       "      <td>pct_14 sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>305</td>\n",
       "      <td>pct_26 othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>286</td>\n",
       "      <td>pct_14 pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>303</td>\n",
       "      <td>pct_26 knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>302</td>\n",
       "      <td>pct_26 sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>301</td>\n",
       "      <td>pct_26 pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>300</td>\n",
       "      <td>pct_26 pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>299</td>\n",
       "      <td>pct_26 pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>287</td>\n",
       "      <td>pct_14 othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>288</td>\n",
       "      <td>pct_14 pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>298</td>\n",
       "      <td>pct_26 descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>297</td>\n",
       "      <td>pct_26 trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>296</td>\n",
       "      <td>pct_26 sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>343</td>\n",
       "      <td>contrabn sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>295</td>\n",
       "      <td>pct_26 contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>294</td>\n",
       "      <td>pct_26 pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>289</td>\n",
       "      <td>pct_14 age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>290</td>\n",
       "      <td>pct_14 pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>291</td>\n",
       "      <td>pct_14 descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>304</td>\n",
       "      <td>pct_26 pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>306</td>\n",
       "      <td>pct_26 pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>322</td>\n",
       "      <td>pf_drwep othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>307</td>\n",
       "      <td>pct_26 age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>321</td>\n",
       "      <td>pf_drwep pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>320</td>\n",
       "      <td>pf_drwep knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>319</td>\n",
       "      <td>pf_drwep sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>318</td>\n",
       "      <td>pf_drwep pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>317</td>\n",
       "      <td>pf_drwep pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>316</td>\n",
       "      <td>pf_drwep pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>283</td>\n",
       "      <td>pct_14 pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>315</td>\n",
       "      <td>pf_drwep descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>314</td>\n",
       "      <td>pf_drwep trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>284</td>\n",
       "      <td>pct_14 sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>313</td>\n",
       "      <td>pf_drwep sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>312</td>\n",
       "      <td>pf_drwep contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>311</td>\n",
       "      <td>pf_drwep^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>285</td>\n",
       "      <td>pct_14 knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>310</td>\n",
       "      <td>pct_26 sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>309</td>\n",
       "      <td>pct_26 descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>308</td>\n",
       "      <td>pct_26 pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>342</td>\n",
       "      <td>contrabn descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>364</td>\n",
       "      <td>trhsloc_Neither sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>344</td>\n",
       "      <td>sb_admis^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>403</td>\n",
       "      <td>pct_13 othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>429</td>\n",
       "      <td>knifcuti pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>428</td>\n",
       "      <td>knifcuti^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>427</td>\n",
       "      <td>sb_outln sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>426</td>\n",
       "      <td>sb_outln descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>425</td>\n",
       "      <td>sb_outln pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>424</td>\n",
       "      <td>sb_outln age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>422</td>\n",
       "      <td>sb_outln othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>421</td>\n",
       "      <td>sb_outln pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>420</td>\n",
       "      <td>sb_outln knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>419</td>\n",
       "      <td>sb_outln^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>418</td>\n",
       "      <td>pct_52 sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>417</td>\n",
       "      <td>pct_52 descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>416</td>\n",
       "      <td>pct_52 pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>414</td>\n",
       "      <td>pct_52 pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>413</td>\n",
       "      <td>pct_52 othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>412</td>\n",
       "      <td>pct_52 pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>411</td>\n",
       "      <td>pct_52 knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>410</td>\n",
       "      <td>pct_52 sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>409</td>\n",
       "      <td>pct_52^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>408</td>\n",
       "      <td>pct_13 sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>406</td>\n",
       "      <td>pct_13 pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>430</td>\n",
       "      <td>knifcuti othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>431</td>\n",
       "      <td>knifcuti pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>433</td>\n",
       "      <td>knifcuti pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>449</td>\n",
       "      <td>pf_hcuff^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>462</td>\n",
       "      <td>descr_CPW sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>461</td>\n",
       "      <td>descr_CPW^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>460</td>\n",
       "      <td>pct_25 sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>459</td>\n",
       "      <td>pct_25 descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>458</td>\n",
       "      <td>pct_25^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>455</td>\n",
       "      <td>age pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>453</td>\n",
       "      <td>pf_hcuff sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>452</td>\n",
       "      <td>pf_hcuff descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>451</td>\n",
       "      <td>pf_hcuff pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>448</td>\n",
       "      <td>othrweap sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>435</td>\n",
       "      <td>knifcuti sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>447</td>\n",
       "      <td>othrweap descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>446</td>\n",
       "      <td>othrweap pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>444</td>\n",
       "      <td>othrweap pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>443</td>\n",
       "      <td>othrweap^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>441</td>\n",
       "      <td>pct_106 descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>440</td>\n",
       "      <td>pct_106 pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>438</td>\n",
       "      <td>pct_106 pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>437</td>\n",
       "      <td>pct_106 othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>436</td>\n",
       "      <td>pct_106^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>404</td>\n",
       "      <td>pct_13 pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>402</td>\n",
       "      <td>pct_13 pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>345</td>\n",
       "      <td>sb_admis trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>401</td>\n",
       "      <td>pct_13 knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>373</td>\n",
       "      <td>descr_PETIT LARCENY^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>372</td>\n",
       "      <td>trhsloc_Neither sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>370</td>\n",
       "      <td>trhsloc_Neither pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>368</td>\n",
       "      <td>trhsloc_Neither pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>365</td>\n",
       "      <td>trhsloc_Neither knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>254</td>\n",
       "      <td>ac_inves^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>363</td>\n",
       "      <td>trhsloc_Neither pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>362</td>\n",
       "      <td>trhsloc_Neither pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>361</td>\n",
       "      <td>trhsloc_Neither pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>360</td>\n",
       "      <td>trhsloc_Neither descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>358</td>\n",
       "      <td>sb_admis sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>357</td>\n",
       "      <td>sb_admis descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>356</td>\n",
       "      <td>sb_admis pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>354</td>\n",
       "      <td>sb_admis pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>352</td>\n",
       "      <td>sb_admis pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>351</td>\n",
       "      <td>sb_admis knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>350</td>\n",
       "      <td>sb_admis sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>349</td>\n",
       "      <td>sb_admis pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>348</td>\n",
       "      <td>sb_admis pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>347</td>\n",
       "      <td>sb_admis pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>346</td>\n",
       "      <td>sb_admis descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>374</td>\n",
       "      <td>descr_PETIT LARCENY pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>375</td>\n",
       "      <td>descr_PETIT LARCENY pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>376</td>\n",
       "      <td>descr_PETIT LARCENY pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>390</td>\n",
       "      <td>pistol knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>400</td>\n",
       "      <td>pct_13 sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>399</td>\n",
       "      <td>pct_13 pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>398</td>\n",
       "      <td>pct_13^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>397</td>\n",
       "      <td>pistol sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>396</td>\n",
       "      <td>pistol descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>395</td>\n",
       "      <td>pistol pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>393</td>\n",
       "      <td>pistol pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>392</td>\n",
       "      <td>pistol othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>391</td>\n",
       "      <td>pistol pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>389</td>\n",
       "      <td>pistol sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>377</td>\n",
       "      <td>descr_PETIT LARCENY sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>388</td>\n",
       "      <td>pistol pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>387</td>\n",
       "      <td>pistol pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>385</td>\n",
       "      <td>descr_PETIT LARCENY sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>384</td>\n",
       "      <td>descr_PETIT LARCENY descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>383</td>\n",
       "      <td>descr_PETIT LARCENY pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>381</td>\n",
       "      <td>descr_PETIT LARCENY pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>380</td>\n",
       "      <td>descr_PETIT LARCENY othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>379</td>\n",
       "      <td>descr_PETIT LARCENY pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>378</td>\n",
       "      <td>descr_PETIT LARCENY knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>255</td>\n",
       "      <td>ac_inves pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>232</td>\n",
       "      <td>sb_other sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>253</td>\n",
       "      <td>descr_THEFT OF SERVICES sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>67</td>\n",
       "      <td>ac_time pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>96</td>\n",
       "      <td>pct_7 pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>95</td>\n",
       "      <td>pct_7 pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>94</td>\n",
       "      <td>pct_7 pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>93</td>\n",
       "      <td>pct_7 ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>92</td>\n",
       "      <td>pct_7 descr_THEFT OF SERVICES</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>91</td>\n",
       "      <td>pct_7 sb_other</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>90</td>\n",
       "      <td>pct_7 searched</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>89</td>\n",
       "      <td>pct_7 cs_casng</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>88</td>\n",
       "      <td>pct_7 pct_61</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>87</td>\n",
       "      <td>pct_7 weight</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>86</td>\n",
       "      <td>pct_7^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>84</td>\n",
       "      <td>ac_time descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>83</td>\n",
       "      <td>ac_time pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>80</td>\n",
       "      <td>ac_time othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>79</td>\n",
       "      <td>ac_time pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>78</td>\n",
       "      <td>ac_time knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>76</td>\n",
       "      <td>ac_time pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>75</td>\n",
       "      <td>ac_time pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>74</td>\n",
       "      <td>ac_time pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>73</td>\n",
       "      <td>ac_time descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>71</td>\n",
       "      <td>ac_time sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>70</td>\n",
       "      <td>ac_time contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>69</td>\n",
       "      <td>ac_time pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>97</td>\n",
       "      <td>pct_7 contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>98</td>\n",
       "      <td>pct_7 sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>99</td>\n",
       "      <td>pct_7 trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>112</td>\n",
       "      <td>pct_7 sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>130</td>\n",
       "      <td>weight sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>129</td>\n",
       "      <td>weight pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>128</td>\n",
       "      <td>weight pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>126</td>\n",
       "      <td>weight descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>124</td>\n",
       "      <td>weight sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>123</td>\n",
       "      <td>weight contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>122</td>\n",
       "      <td>weight pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>121</td>\n",
       "      <td>weight pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>120</td>\n",
       "      <td>weight pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>114</td>\n",
       "      <td>weight pct_61</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>111</td>\n",
       "      <td>pct_7 descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>100</td>\n",
       "      <td>pct_7 descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>110</td>\n",
       "      <td>pct_7 pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>109</td>\n",
       "      <td>pct_7 age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>108</td>\n",
       "      <td>pct_7 pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>107</td>\n",
       "      <td>pct_7 othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>106</td>\n",
       "      <td>pct_7 pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>105</td>\n",
       "      <td>pct_7 knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>104</td>\n",
       "      <td>pct_7 sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>103</td>\n",
       "      <td>pct_7 pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>102</td>\n",
       "      <td>pct_7 pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>101</td>\n",
       "      <td>pct_7 pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>68</td>\n",
       "      <td>ac_time pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>65</td>\n",
       "      <td>ac_time descr_THEFT OF SERVICES</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>252</td>\n",
       "      <td>descr_THEFT OF SERVICES descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>64</td>\n",
       "      <td>ac_time sb_other</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>33</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_61</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>30</td>\n",
       "      <td>descr_CRIMINAL TRESPASS ac_time</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>29</td>\n",
       "      <td>descr_CRIMINAL TRESPASS^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>28</td>\n",
       "      <td>sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>27</td>\n",
       "      <td>descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>26</td>\n",
       "      <td>pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>23</td>\n",
       "      <td>othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>20</td>\n",
       "      <td>sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>19</td>\n",
       "      <td>pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>18</td>\n",
       "      <td>pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>16</td>\n",
       "      <td>descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>15</td>\n",
       "      <td>trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>14</td>\n",
       "      <td>sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>11</td>\n",
       "      <td>pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>10</td>\n",
       "      <td>pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>9</td>\n",
       "      <td>ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>8</td>\n",
       "      <td>descr_THEFT OF SERVICES</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>7</td>\n",
       "      <td>sb_other</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>6</td>\n",
       "      <td>searched</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>5</td>\n",
       "      <td>cs_casng</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>4</td>\n",
       "      <td>pct_61</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>3</td>\n",
       "      <td>weight</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2</td>\n",
       "      <td>pct_7</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>34</td>\n",
       "      <td>descr_CRIMINAL TRESPASS cs_casng</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>35</td>\n",
       "      <td>descr_CRIMINAL TRESPASS searched</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>37</td>\n",
       "      <td>descr_CRIMINAL TRESPASS descr_THEFT OF SERVICES</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>50</td>\n",
       "      <td>descr_CRIMINAL TRESPASS knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>63</td>\n",
       "      <td>ac_time searched</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>61</td>\n",
       "      <td>ac_time pct_61</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>60</td>\n",
       "      <td>ac_time weight</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>59</td>\n",
       "      <td>ac_time pct_7</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>57</td>\n",
       "      <td>descr_CRIMINAL TRESPASS sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>56</td>\n",
       "      <td>descr_CRIMINAL TRESPASS descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>55</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>53</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>52</td>\n",
       "      <td>descr_CRIMINAL TRESPASS othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>51</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>49</td>\n",
       "      <td>descr_CRIMINAL TRESPASS sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>38</td>\n",
       "      <td>descr_CRIMINAL TRESPASS ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>48</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>47</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>46</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>45</td>\n",
       "      <td>descr_CRIMINAL TRESPASS descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>44</td>\n",
       "      <td>descr_CRIMINAL TRESPASS trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>43</td>\n",
       "      <td>descr_CRIMINAL TRESPASS sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>42</td>\n",
       "      <td>descr_CRIMINAL TRESPASS contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>41</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>40</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>39</td>\n",
       "      <td>descr_CRIMINAL TRESPASS pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>136</td>\n",
       "      <td>weight pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>138</td>\n",
       "      <td>weight sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>139</td>\n",
       "      <td>pct_61^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>140</td>\n",
       "      <td>pct_61 cs_casng</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>219</td>\n",
       "      <td>sb_other trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>218</td>\n",
       "      <td>sb_other sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>217</td>\n",
       "      <td>sb_other contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>216</td>\n",
       "      <td>sb_other pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>215</td>\n",
       "      <td>sb_other pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>214</td>\n",
       "      <td>sb_other pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>213</td>\n",
       "      <td>sb_other ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>212</td>\n",
       "      <td>sb_other descr_THEFT OF SERVICES</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>211</td>\n",
       "      <td>sb_other^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>210</td>\n",
       "      <td>searched sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>209</td>\n",
       "      <td>searched descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>208</td>\n",
       "      <td>searched pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>206</td>\n",
       "      <td>searched pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>205</td>\n",
       "      <td>searched othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>204</td>\n",
       "      <td>searched pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>203</td>\n",
       "      <td>searched knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>202</td>\n",
       "      <td>searched sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>201</td>\n",
       "      <td>searched pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>200</td>\n",
       "      <td>searched pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>199</td>\n",
       "      <td>searched pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>198</td>\n",
       "      <td>searched descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>197</td>\n",
       "      <td>searched trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>196</td>\n",
       "      <td>searched sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>220</td>\n",
       "      <td>sb_other descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>221</td>\n",
       "      <td>sb_other pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>222</td>\n",
       "      <td>sb_other pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>240</td>\n",
       "      <td>descr_THEFT OF SERVICES trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>251</td>\n",
       "      <td>descr_THEFT OF SERVICES pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>250</td>\n",
       "      <td>descr_THEFT OF SERVICES age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>249</td>\n",
       "      <td>descr_THEFT OF SERVICES pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>248</td>\n",
       "      <td>descr_THEFT OF SERVICES othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>247</td>\n",
       "      <td>descr_THEFT OF SERVICES pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>246</td>\n",
       "      <td>descr_THEFT OF SERVICES knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>245</td>\n",
       "      <td>descr_THEFT OF SERVICES sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>244</td>\n",
       "      <td>descr_THEFT OF SERVICES pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>242</td>\n",
       "      <td>descr_THEFT OF SERVICES pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>241</td>\n",
       "      <td>descr_THEFT OF SERVICES descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>239</td>\n",
       "      <td>descr_THEFT OF SERVICES sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>223</td>\n",
       "      <td>sb_other pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>238</td>\n",
       "      <td>descr_THEFT OF SERVICES contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>237</td>\n",
       "      <td>descr_THEFT OF SERVICES pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>236</td>\n",
       "      <td>descr_THEFT OF SERVICES pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>235</td>\n",
       "      <td>descr_THEFT OF SERVICES pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>234</td>\n",
       "      <td>descr_THEFT OF SERVICES ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>233</td>\n",
       "      <td>descr_THEFT OF SERVICES^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>228</td>\n",
       "      <td>sb_other pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>226</td>\n",
       "      <td>sb_other pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>225</td>\n",
       "      <td>sb_other knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>224</td>\n",
       "      <td>sb_other sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>195</td>\n",
       "      <td>searched contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>194</td>\n",
       "      <td>searched pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>193</td>\n",
       "      <td>searched pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>153</td>\n",
       "      <td>pct_61 pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>163</td>\n",
       "      <td>pct_61 sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>162</td>\n",
       "      <td>pct_61 descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>161</td>\n",
       "      <td>pct_61 pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>160</td>\n",
       "      <td>pct_61 age</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>159</td>\n",
       "      <td>pct_61 pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>158</td>\n",
       "      <td>pct_61 othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>157</td>\n",
       "      <td>pct_61 pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>156</td>\n",
       "      <td>pct_61 knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>155</td>\n",
       "      <td>pct_61 sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>154</td>\n",
       "      <td>pct_61 pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>152</td>\n",
       "      <td>pct_61 pistol</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>165</td>\n",
       "      <td>cs_casng searched</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>151</td>\n",
       "      <td>pct_61 descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>150</td>\n",
       "      <td>pct_61 trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>149</td>\n",
       "      <td>pct_61 sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>148</td>\n",
       "      <td>pct_61 contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>147</td>\n",
       "      <td>pct_61 pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>146</td>\n",
       "      <td>pct_61 pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>145</td>\n",
       "      <td>pct_61 pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>144</td>\n",
       "      <td>pct_61 ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>142</td>\n",
       "      <td>pct_61 sb_other</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>141</td>\n",
       "      <td>pct_61 searched</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>164</td>\n",
       "      <td>cs_casng^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>166</td>\n",
       "      <td>cs_casng sb_other</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>192</td>\n",
       "      <td>searched pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>180</td>\n",
       "      <td>cs_casng knifcuti</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>191</td>\n",
       "      <td>searched ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>190</td>\n",
       "      <td>searched descr_THEFT OF SERVICES</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>189</td>\n",
       "      <td>searched sb_other</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>188</td>\n",
       "      <td>searched^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>187</td>\n",
       "      <td>cs_casng sb_hdobj</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>186</td>\n",
       "      <td>cs_casng descr_CPW</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>185</td>\n",
       "      <td>cs_casng pct_25</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>183</td>\n",
       "      <td>cs_casng pf_hcuff</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>182</td>\n",
       "      <td>cs_casng othrweap</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>181</td>\n",
       "      <td>cs_casng pct_106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>179</td>\n",
       "      <td>cs_casng sb_outln</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>167</td>\n",
       "      <td>cs_casng descr_THEFT OF SERVICES</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>178</td>\n",
       "      <td>cs_casng pct_52</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>177</td>\n",
       "      <td>cs_casng pct_13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>175</td>\n",
       "      <td>cs_casng descr_PETIT LARCENY</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>174</td>\n",
       "      <td>cs_casng trhsloc_Neither</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>173</td>\n",
       "      <td>cs_casng sb_admis</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>172</td>\n",
       "      <td>cs_casng contrabn</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>171</td>\n",
       "      <td>cs_casng pf_drwep</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>170</td>\n",
       "      <td>cs_casng pct_26</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>169</td>\n",
       "      <td>cs_casng pct_14</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>168</td>\n",
       "      <td>cs_casng ac_inves</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>463</td>\n",
       "      <td>sb_hdobj^2</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                         features  \\\n",
       "0      116                                  weight searched   \n",
       "1      340                                     contrabn age   \n",
       "2       54                      descr_CRIMINAL TRESPASS age   \n",
       "3      450                                     pf_hcuff age   \n",
       "4       13                                         contrabn   \n",
       "5      457                                     age sb_hdobj   \n",
       "6      125                           weight trhsloc_Neither   \n",
       "7      432                                     knifcuti age   \n",
       "8      135                                       weight age   \n",
       "9      118                   weight descr_THEFT OF SERVICES   \n",
       "10     382                          descr_PETIT LARCENY age   \n",
       "11     137                                 weight descr_CPW   \n",
       "12     386                                         pistol^2   \n",
       "13     445                                     othrweap age   \n",
       "14     127                                    weight pistol   \n",
       "15     407                                 pct_13 descr_CPW   \n",
       "16      21                                         knifcuti   \n",
       "17     369                              trhsloc_Neither age   \n",
       "18      36                 descr_CRIMINAL TRESPASS sb_other   \n",
       "19     367                         trhsloc_Neither othrweap   \n",
       "20     207                                     searched age   \n",
       "21     131                                  weight knifcuti   \n",
       "22      62                                 ac_time cs_casng   \n",
       "23     134                                  weight pf_hcuff   \n",
       "24      82                                      ac_time age   \n",
       "25     231                               sb_other descr_CPW   \n",
       "26     439                                      pct_106 age   \n",
       "27     442                                 pct_106 sb_hdobj   \n",
       "28      12                                         pf_drwep   \n",
       "29     133                                  weight othrweap   \n",
       "30     119                                  weight ac_inves   \n",
       "31      72                          ac_time trhsloc_Neither   \n",
       "32     456                                    age descr_CPW   \n",
       "33     454                                            age^2   \n",
       "34      17                                           pistol   \n",
       "35     434                               knifcuti descr_CPW   \n",
       "36     143                   pct_61 descr_THEFT OF SERVICES   \n",
       "37     359                                trhsloc_Neither^2   \n",
       "38     270                                     ac_inves age   \n",
       "39      25                                              age   \n",
       "40      22                                          pct_106   \n",
       "41     366                          trhsloc_Neither pct_106   \n",
       "42     371                        trhsloc_Neither descr_CPW   \n",
       "43      81                                 ac_time pf_hcuff   \n",
       "44     227                                sb_other othrweap   \n",
       "45     394                                       pistol age   \n",
       "46      66                                 ac_time ac_inves   \n",
       "47     415                                       pct_52 age   \n",
       "48     115                                  weight cs_casng   \n",
       "49     184                                     cs_casng age   \n",
       "50      77                                 ac_time sb_outln   \n",
       "51     272                               ac_inves descr_CPW   \n",
       "52     243                   descr_THEFT OF SERVICES pct_13   \n",
       "53      31                    descr_CRIMINAL TRESPASS pct_7   \n",
       "54      32                   descr_CRIMINAL TRESPASS weight   \n",
       "55     113                                         weight^2   \n",
       "56     355                                     sb_admis age   \n",
       "57     334                                  contrabn pct_52   \n",
       "58     229                                     sb_other age   \n",
       "59      24                                         pf_hcuff   \n",
       "60     405                                       pct_13 age   \n",
       "61     117                                  weight sb_other   \n",
       "62       1                                          ac_time   \n",
       "63     423                                sb_outln pf_hcuff   \n",
       "64     353                                sb_admis othrweap   \n",
       "65     230                                  sb_other pct_25   \n",
       "66     132                                   weight pct_106   \n",
       "67     176                                  cs_casng pistol   \n",
       "68      58                                        ac_time^2   \n",
       "69       0                          descr_CRIMINAL TRESPASS   \n",
       "70      85                                 ac_time sb_hdobj   \n",
       "71     293                                         pct_26^2   \n",
       "72     264                                  ac_inves pct_52   \n",
       "73     266                                ac_inves knifcuti   \n",
       "74     267                                 ac_inves pct_106   \n",
       "75     328                                       contrabn^2   \n",
       "76     327                                pf_drwep sb_hdobj   \n",
       "77     326                               pf_drwep descr_CPW   \n",
       "78     268                                ac_inves othrweap   \n",
       "79     325                                  pf_drwep pct_25   \n",
       "80     269                                ac_inves pf_hcuff   \n",
       "81     324                                     pf_drwep age   \n",
       "82     271                                  ac_inves pct_25   \n",
       "83     273                                ac_inves sb_hdobj   \n",
       "84     323                                pf_drwep pf_hcuff   \n",
       "85     274                                         pct_14^2   \n",
       "86     275                                    pct_14 pct_26   \n",
       "87     276                                  pct_14 pf_drwep   \n",
       "88     277                                  pct_14 contrabn   \n",
       "89     278                                  pct_14 sb_admis   \n",
       "90     279                           pct_14 trhsloc_Neither   \n",
       "91     265                                ac_inves sb_outln   \n",
       "92     263                                  ac_inves pct_13   \n",
       "93     281                                    pct_14 pistol   \n",
       "94     329                                contrabn sb_admis   \n",
       "95     341                                  contrabn pct_25   \n",
       "96     256                                  ac_inves pct_26   \n",
       "97     339                                contrabn pf_hcuff   \n",
       "98     338                                contrabn othrweap   \n",
       "99     337                                 contrabn pct_106   \n",
       "100    336                                contrabn knifcuti   \n",
       "101    335                                contrabn sb_outln   \n",
       "102    257                                ac_inves pf_drwep   \n",
       "103    258                                ac_inves contrabn   \n",
       "104    333                                  contrabn pct_13   \n",
       "105    259                                ac_inves sb_admis   \n",
       "106    260                         ac_inves trhsloc_Neither   \n",
       "107    332                                  contrabn pistol   \n",
       "108    331                     contrabn descr_PETIT LARCENY   \n",
       "109    330                         contrabn trhsloc_Neither   \n",
       "110    261                     ac_inves descr_PETIT LARCENY   \n",
       "111    262                                  ac_inves pistol   \n",
       "112    280                       pct_14 descr_PETIT LARCENY   \n",
       "113    282                                    pct_14 pct_13   \n",
       "114    292                                  pct_14 sb_hdobj   \n",
       "115    305                                  pct_26 othrweap   \n",
       "116    286                                   pct_14 pct_106   \n",
       "117    303                                  pct_26 knifcuti   \n",
       "118    302                                  pct_26 sb_outln   \n",
       "119    301                                    pct_26 pct_52   \n",
       "120    300                                    pct_26 pct_13   \n",
       "121    299                                    pct_26 pistol   \n",
       "122    287                                  pct_14 othrweap   \n",
       "123    288                                  pct_14 pf_hcuff   \n",
       "124    298                       pct_26 descr_PETIT LARCENY   \n",
       "125    297                           pct_26 trhsloc_Neither   \n",
       "126    296                                  pct_26 sb_admis   \n",
       "127    343                                contrabn sb_hdobj   \n",
       "128    295                                  pct_26 contrabn   \n",
       "129    294                                  pct_26 pf_drwep   \n",
       "130    289                                       pct_14 age   \n",
       "131    290                                    pct_14 pct_25   \n",
       "132    291                                 pct_14 descr_CPW   \n",
       "133    304                                   pct_26 pct_106   \n",
       "134    306                                  pct_26 pf_hcuff   \n",
       "135    322                                pf_drwep othrweap   \n",
       "136    307                                       pct_26 age   \n",
       "137    321                                 pf_drwep pct_106   \n",
       "138    320                                pf_drwep knifcuti   \n",
       "139    319                                pf_drwep sb_outln   \n",
       "140    318                                  pf_drwep pct_52   \n",
       "141    317                                  pf_drwep pct_13   \n",
       "142    316                                  pf_drwep pistol   \n",
       "143    283                                    pct_14 pct_52   \n",
       "144    315                     pf_drwep descr_PETIT LARCENY   \n",
       "145    314                         pf_drwep trhsloc_Neither   \n",
       "146    284                                  pct_14 sb_outln   \n",
       "147    313                                pf_drwep sb_admis   \n",
       "148    312                                pf_drwep contrabn   \n",
       "149    311                                       pf_drwep^2   \n",
       "150    285                                  pct_14 knifcuti   \n",
       "151    310                                  pct_26 sb_hdobj   \n",
       "152    309                                 pct_26 descr_CPW   \n",
       "153    308                                    pct_26 pct_25   \n",
       "154    342                               contrabn descr_CPW   \n",
       "155    364                         trhsloc_Neither sb_outln   \n",
       "156    344                                       sb_admis^2   \n",
       "157    403                                  pct_13 othrweap   \n",
       "158    429                                 knifcuti pct_106   \n",
       "159    428                                       knifcuti^2   \n",
       "160    427                                sb_outln sb_hdobj   \n",
       "161    426                               sb_outln descr_CPW   \n",
       "162    425                                  sb_outln pct_25   \n",
       "163    424                                     sb_outln age   \n",
       "164    422                                sb_outln othrweap   \n",
       "165    421                                 sb_outln pct_106   \n",
       "166    420                                sb_outln knifcuti   \n",
       "167    419                                       sb_outln^2   \n",
       "168    418                                  pct_52 sb_hdobj   \n",
       "169    417                                 pct_52 descr_CPW   \n",
       "170    416                                    pct_52 pct_25   \n",
       "171    414                                  pct_52 pf_hcuff   \n",
       "172    413                                  pct_52 othrweap   \n",
       "173    412                                   pct_52 pct_106   \n",
       "174    411                                  pct_52 knifcuti   \n",
       "175    410                                  pct_52 sb_outln   \n",
       "176    409                                         pct_52^2   \n",
       "177    408                                  pct_13 sb_hdobj   \n",
       "178    406                                    pct_13 pct_25   \n",
       "179    430                                knifcuti othrweap   \n",
       "180    431                                knifcuti pf_hcuff   \n",
       "181    433                                  knifcuti pct_25   \n",
       "182    449                                       pf_hcuff^2   \n",
       "183    462                               descr_CPW sb_hdobj   \n",
       "184    461                                      descr_CPW^2   \n",
       "185    460                                  pct_25 sb_hdobj   \n",
       "186    459                                 pct_25 descr_CPW   \n",
       "187    458                                         pct_25^2   \n",
       "188    455                                       age pct_25   \n",
       "189    453                                pf_hcuff sb_hdobj   \n",
       "190    452                               pf_hcuff descr_CPW   \n",
       "191    451                                  pf_hcuff pct_25   \n",
       "192    448                                othrweap sb_hdobj   \n",
       "193    435                                knifcuti sb_hdobj   \n",
       "194    447                               othrweap descr_CPW   \n",
       "195    446                                  othrweap pct_25   \n",
       "196    444                                othrweap pf_hcuff   \n",
       "197    443                                       othrweap^2   \n",
       "198    441                                pct_106 descr_CPW   \n",
       "199    440                                   pct_106 pct_25   \n",
       "200    438                                 pct_106 pf_hcuff   \n",
       "201    437                                 pct_106 othrweap   \n",
       "202    436                                        pct_106^2   \n",
       "203    404                                  pct_13 pf_hcuff   \n",
       "204    402                                   pct_13 pct_106   \n",
       "205    345                         sb_admis trhsloc_Neither   \n",
       "206    401                                  pct_13 knifcuti   \n",
       "207    373                            descr_PETIT LARCENY^2   \n",
       "208    372                         trhsloc_Neither sb_hdobj   \n",
       "209    370                           trhsloc_Neither pct_25   \n",
       "210    368                         trhsloc_Neither pf_hcuff   \n",
       "211    365                         trhsloc_Neither knifcuti   \n",
       "212    254                                       ac_inves^2   \n",
       "213    363                           trhsloc_Neither pct_52   \n",
       "214    362                           trhsloc_Neither pct_13   \n",
       "215    361                           trhsloc_Neither pistol   \n",
       "216    360              trhsloc_Neither descr_PETIT LARCENY   \n",
       "217    358                                sb_admis sb_hdobj   \n",
       "218    357                               sb_admis descr_CPW   \n",
       "219    356                                  sb_admis pct_25   \n",
       "220    354                                sb_admis pf_hcuff   \n",
       "221    352                                 sb_admis pct_106   \n",
       "222    351                                sb_admis knifcuti   \n",
       "223    350                                sb_admis sb_outln   \n",
       "224    349                                  sb_admis pct_52   \n",
       "225    348                                  sb_admis pct_13   \n",
       "226    347                                  sb_admis pistol   \n",
       "227    346                     sb_admis descr_PETIT LARCENY   \n",
       "228    374                       descr_PETIT LARCENY pistol   \n",
       "229    375                       descr_PETIT LARCENY pct_13   \n",
       "230    376                       descr_PETIT LARCENY pct_52   \n",
       "231    390                                  pistol knifcuti   \n",
       "232    400                                  pct_13 sb_outln   \n",
       "233    399                                    pct_13 pct_52   \n",
       "234    398                                         pct_13^2   \n",
       "235    397                                  pistol sb_hdobj   \n",
       "236    396                                 pistol descr_CPW   \n",
       "237    395                                    pistol pct_25   \n",
       "238    393                                  pistol pf_hcuff   \n",
       "239    392                                  pistol othrweap   \n",
       "240    391                                   pistol pct_106   \n",
       "241    389                                  pistol sb_outln   \n",
       "242    377                     descr_PETIT LARCENY sb_outln   \n",
       "243    388                                    pistol pct_52   \n",
       "244    387                                    pistol pct_13   \n",
       "245    385                     descr_PETIT LARCENY sb_hdobj   \n",
       "246    384                    descr_PETIT LARCENY descr_CPW   \n",
       "247    383                       descr_PETIT LARCENY pct_25   \n",
       "248    381                     descr_PETIT LARCENY pf_hcuff   \n",
       "249    380                     descr_PETIT LARCENY othrweap   \n",
       "250    379                      descr_PETIT LARCENY pct_106   \n",
       "251    378                     descr_PETIT LARCENY knifcuti   \n",
       "252    255                                  ac_inves pct_14   \n",
       "253    232                                sb_other sb_hdobj   \n",
       "254    253                 descr_THEFT OF SERVICES sb_hdobj   \n",
       "255     67                                   ac_time pct_14   \n",
       "256     96                                   pct_7 pf_drwep   \n",
       "257     95                                     pct_7 pct_26   \n",
       "258     94                                     pct_7 pct_14   \n",
       "259     93                                   pct_7 ac_inves   \n",
       "260     92                    pct_7 descr_THEFT OF SERVICES   \n",
       "261     91                                   pct_7 sb_other   \n",
       "262     90                                   pct_7 searched   \n",
       "263     89                                   pct_7 cs_casng   \n",
       "264     88                                     pct_7 pct_61   \n",
       "265     87                                     pct_7 weight   \n",
       "266     86                                          pct_7^2   \n",
       "267     84                                ac_time descr_CPW   \n",
       "268     83                                   ac_time pct_25   \n",
       "269     80                                 ac_time othrweap   \n",
       "270     79                                  ac_time pct_106   \n",
       "271     78                                 ac_time knifcuti   \n",
       "272     76                                   ac_time pct_52   \n",
       "273     75                                   ac_time pct_13   \n",
       "274     74                                   ac_time pistol   \n",
       "275     73                      ac_time descr_PETIT LARCENY   \n",
       "276     71                                 ac_time sb_admis   \n",
       "277     70                                 ac_time contrabn   \n",
       "278     69                                 ac_time pf_drwep   \n",
       "279     97                                   pct_7 contrabn   \n",
       "280     98                                   pct_7 sb_admis   \n",
       "281     99                            pct_7 trhsloc_Neither   \n",
       "282    112                                   pct_7 sb_hdobj   \n",
       "283    130                                  weight sb_outln   \n",
       "284    129                                    weight pct_52   \n",
       "285    128                                    weight pct_13   \n",
       "286    126                       weight descr_PETIT LARCENY   \n",
       "287    124                                  weight sb_admis   \n",
       "288    123                                  weight contrabn   \n",
       "289    122                                  weight pf_drwep   \n",
       "290    121                                    weight pct_26   \n",
       "291    120                                    weight pct_14   \n",
       "292    114                                    weight pct_61   \n",
       "293    111                                  pct_7 descr_CPW   \n",
       "294    100                        pct_7 descr_PETIT LARCENY   \n",
       "295    110                                     pct_7 pct_25   \n",
       "296    109                                        pct_7 age   \n",
       "297    108                                   pct_7 pf_hcuff   \n",
       "298    107                                   pct_7 othrweap   \n",
       "299    106                                    pct_7 pct_106   \n",
       "300    105                                   pct_7 knifcuti   \n",
       "301    104                                   pct_7 sb_outln   \n",
       "302    103                                     pct_7 pct_52   \n",
       "303    102                                     pct_7 pct_13   \n",
       "304    101                                     pct_7 pistol   \n",
       "305     68                                   ac_time pct_26   \n",
       "306     65                  ac_time descr_THEFT OF SERVICES   \n",
       "307    252                descr_THEFT OF SERVICES descr_CPW   \n",
       "308     64                                 ac_time sb_other   \n",
       "309     33                   descr_CRIMINAL TRESPASS pct_61   \n",
       "310     30                  descr_CRIMINAL TRESPASS ac_time   \n",
       "311     29                        descr_CRIMINAL TRESPASS^2   \n",
       "312     28                                         sb_hdobj   \n",
       "313     27                                        descr_CPW   \n",
       "314     26                                           pct_25   \n",
       "315     23                                         othrweap   \n",
       "316     20                                         sb_outln   \n",
       "317     19                                           pct_52   \n",
       "318     18                                           pct_13   \n",
       "319     16                              descr_PETIT LARCENY   \n",
       "320     15                                  trhsloc_Neither   \n",
       "321     14                                         sb_admis   \n",
       "322     11                                           pct_26   \n",
       "323     10                                           pct_14   \n",
       "324      9                                         ac_inves   \n",
       "325      8                          descr_THEFT OF SERVICES   \n",
       "326      7                                         sb_other   \n",
       "327      6                                         searched   \n",
       "328      5                                         cs_casng   \n",
       "329      4                                           pct_61   \n",
       "330      3                                           weight   \n",
       "331      2                                            pct_7   \n",
       "332     34                 descr_CRIMINAL TRESPASS cs_casng   \n",
       "333     35                 descr_CRIMINAL TRESPASS searched   \n",
       "334     37  descr_CRIMINAL TRESPASS descr_THEFT OF SERVICES   \n",
       "335     50                 descr_CRIMINAL TRESPASS knifcuti   \n",
       "336     63                                 ac_time searched   \n",
       "337     61                                   ac_time pct_61   \n",
       "338     60                                   ac_time weight   \n",
       "339     59                                    ac_time pct_7   \n",
       "340     57                 descr_CRIMINAL TRESPASS sb_hdobj   \n",
       "341     56                descr_CRIMINAL TRESPASS descr_CPW   \n",
       "342     55                   descr_CRIMINAL TRESPASS pct_25   \n",
       "343     53                 descr_CRIMINAL TRESPASS pf_hcuff   \n",
       "344     52                 descr_CRIMINAL TRESPASS othrweap   \n",
       "345     51                  descr_CRIMINAL TRESPASS pct_106   \n",
       "346     49                 descr_CRIMINAL TRESPASS sb_outln   \n",
       "347     38                 descr_CRIMINAL TRESPASS ac_inves   \n",
       "348     48                   descr_CRIMINAL TRESPASS pct_52   \n",
       "349     47                   descr_CRIMINAL TRESPASS pct_13   \n",
       "350     46                   descr_CRIMINAL TRESPASS pistol   \n",
       "351     45      descr_CRIMINAL TRESPASS descr_PETIT LARCENY   \n",
       "352     44          descr_CRIMINAL TRESPASS trhsloc_Neither   \n",
       "353     43                 descr_CRIMINAL TRESPASS sb_admis   \n",
       "354     42                 descr_CRIMINAL TRESPASS contrabn   \n",
       "355     41                 descr_CRIMINAL TRESPASS pf_drwep   \n",
       "356     40                   descr_CRIMINAL TRESPASS pct_26   \n",
       "357     39                   descr_CRIMINAL TRESPASS pct_14   \n",
       "358    136                                    weight pct_25   \n",
       "359    138                                  weight sb_hdobj   \n",
       "360    139                                         pct_61^2   \n",
       "361    140                                  pct_61 cs_casng   \n",
       "362    219                         sb_other trhsloc_Neither   \n",
       "363    218                                sb_other sb_admis   \n",
       "364    217                                sb_other contrabn   \n",
       "365    216                                sb_other pf_drwep   \n",
       "366    215                                  sb_other pct_26   \n",
       "367    214                                  sb_other pct_14   \n",
       "368    213                                sb_other ac_inves   \n",
       "369    212                 sb_other descr_THEFT OF SERVICES   \n",
       "370    211                                       sb_other^2   \n",
       "371    210                                searched sb_hdobj   \n",
       "372    209                               searched descr_CPW   \n",
       "373    208                                  searched pct_25   \n",
       "374    206                                searched pf_hcuff   \n",
       "375    205                                searched othrweap   \n",
       "376    204                                 searched pct_106   \n",
       "377    203                                searched knifcuti   \n",
       "378    202                                searched sb_outln   \n",
       "379    201                                  searched pct_52   \n",
       "380    200                                  searched pct_13   \n",
       "381    199                                  searched pistol   \n",
       "382    198                     searched descr_PETIT LARCENY   \n",
       "383    197                         searched trhsloc_Neither   \n",
       "384    196                                searched sb_admis   \n",
       "385    220                     sb_other descr_PETIT LARCENY   \n",
       "386    221                                  sb_other pistol   \n",
       "387    222                                  sb_other pct_13   \n",
       "388    240          descr_THEFT OF SERVICES trhsloc_Neither   \n",
       "389    251                   descr_THEFT OF SERVICES pct_25   \n",
       "390    250                      descr_THEFT OF SERVICES age   \n",
       "391    249                 descr_THEFT OF SERVICES pf_hcuff   \n",
       "392    248                 descr_THEFT OF SERVICES othrweap   \n",
       "393    247                  descr_THEFT OF SERVICES pct_106   \n",
       "394    246                 descr_THEFT OF SERVICES knifcuti   \n",
       "395    245                 descr_THEFT OF SERVICES sb_outln   \n",
       "396    244                   descr_THEFT OF SERVICES pct_52   \n",
       "397    242                   descr_THEFT OF SERVICES pistol   \n",
       "398    241      descr_THEFT OF SERVICES descr_PETIT LARCENY   \n",
       "399    239                 descr_THEFT OF SERVICES sb_admis   \n",
       "400    223                                  sb_other pct_52   \n",
       "401    238                 descr_THEFT OF SERVICES contrabn   \n",
       "402    237                 descr_THEFT OF SERVICES pf_drwep   \n",
       "403    236                   descr_THEFT OF SERVICES pct_26   \n",
       "404    235                   descr_THEFT OF SERVICES pct_14   \n",
       "405    234                 descr_THEFT OF SERVICES ac_inves   \n",
       "406    233                        descr_THEFT OF SERVICES^2   \n",
       "407    228                                sb_other pf_hcuff   \n",
       "408    226                                 sb_other pct_106   \n",
       "409    225                                sb_other knifcuti   \n",
       "410    224                                sb_other sb_outln   \n",
       "411    195                                searched contrabn   \n",
       "412    194                                searched pf_drwep   \n",
       "413    193                                  searched pct_26   \n",
       "414    153                                    pct_61 pct_13   \n",
       "415    163                                  pct_61 sb_hdobj   \n",
       "416    162                                 pct_61 descr_CPW   \n",
       "417    161                                    pct_61 pct_25   \n",
       "418    160                                       pct_61 age   \n",
       "419    159                                  pct_61 pf_hcuff   \n",
       "420    158                                  pct_61 othrweap   \n",
       "421    157                                   pct_61 pct_106   \n",
       "422    156                                  pct_61 knifcuti   \n",
       "423    155                                  pct_61 sb_outln   \n",
       "424    154                                    pct_61 pct_52   \n",
       "425    152                                    pct_61 pistol   \n",
       "426    165                                cs_casng searched   \n",
       "427    151                       pct_61 descr_PETIT LARCENY   \n",
       "428    150                           pct_61 trhsloc_Neither   \n",
       "429    149                                  pct_61 sb_admis   \n",
       "430    148                                  pct_61 contrabn   \n",
       "431    147                                  pct_61 pf_drwep   \n",
       "432    146                                    pct_61 pct_26   \n",
       "433    145                                    pct_61 pct_14   \n",
       "434    144                                  pct_61 ac_inves   \n",
       "435    142                                  pct_61 sb_other   \n",
       "436    141                                  pct_61 searched   \n",
       "437    164                                       cs_casng^2   \n",
       "438    166                                cs_casng sb_other   \n",
       "439    192                                  searched pct_14   \n",
       "440    180                                cs_casng knifcuti   \n",
       "441    191                                searched ac_inves   \n",
       "442    190                 searched descr_THEFT OF SERVICES   \n",
       "443    189                                searched sb_other   \n",
       "444    188                                       searched^2   \n",
       "445    187                                cs_casng sb_hdobj   \n",
       "446    186                               cs_casng descr_CPW   \n",
       "447    185                                  cs_casng pct_25   \n",
       "448    183                                cs_casng pf_hcuff   \n",
       "449    182                                cs_casng othrweap   \n",
       "450    181                                 cs_casng pct_106   \n",
       "451    179                                cs_casng sb_outln   \n",
       "452    167                 cs_casng descr_THEFT OF SERVICES   \n",
       "453    178                                  cs_casng pct_52   \n",
       "454    177                                  cs_casng pct_13   \n",
       "455    175                     cs_casng descr_PETIT LARCENY   \n",
       "456    174                         cs_casng trhsloc_Neither   \n",
       "457    173                                cs_casng sb_admis   \n",
       "458    172                                cs_casng contrabn   \n",
       "459    171                                cs_casng pf_drwep   \n",
       "460    170                                  cs_casng pct_26   \n",
       "461    169                                  cs_casng pct_14   \n",
       "462    168                                cs_casng ac_inves   \n",
       "463    463                                       sb_hdobj^2   \n",
       "\n",
       "     feature_importance  \n",
       "0          5.821202e-01  \n",
       "1          9.320754e-02  \n",
       "2          6.870072e-02  \n",
       "3          3.613620e-02  \n",
       "4          2.124799e-02  \n",
       "5          1.739054e-02  \n",
       "6          1.362526e-02  \n",
       "7          1.174297e-02  \n",
       "8          1.167958e-02  \n",
       "9          1.039716e-02  \n",
       "10         1.019650e-02  \n",
       "11         8.655833e-03  \n",
       "12         7.507264e-03  \n",
       "13         6.878391e-03  \n",
       "14         6.732962e-03  \n",
       "15         6.383574e-03  \n",
       "16         6.288697e-03  \n",
       "17         6.253864e-03  \n",
       "18         6.200304e-03  \n",
       "19         4.280606e-03  \n",
       "20         3.840215e-03  \n",
       "21         3.779363e-03  \n",
       "22         3.492739e-03  \n",
       "23         3.073215e-03  \n",
       "24         3.069850e-03  \n",
       "25         2.699331e-03  \n",
       "26         2.674691e-03  \n",
       "27         2.431249e-03  \n",
       "28         2.399020e-03  \n",
       "29         2.277867e-03  \n",
       "30         2.254475e-03  \n",
       "31         2.208082e-03  \n",
       "32         2.057474e-03  \n",
       "33         2.051966e-03  \n",
       "34         1.998359e-03  \n",
       "35         1.704015e-03  \n",
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       "432        0.000000e+00  \n",
       "433        0.000000e+00  \n",
       "434        0.000000e+00  \n",
       "435        0.000000e+00  \n",
       "436        0.000000e+00  \n",
       "437        0.000000e+00  \n",
       "438        0.000000e+00  \n",
       "439        0.000000e+00  \n",
       "440        0.000000e+00  \n",
       "441        0.000000e+00  \n",
       "442        0.000000e+00  \n",
       "443        0.000000e+00  \n",
       "444        0.000000e+00  \n",
       "445        0.000000e+00  \n",
       "446        0.000000e+00  \n",
       "447        0.000000e+00  \n",
       "448        0.000000e+00  \n",
       "449        0.000000e+00  \n",
       "450        0.000000e+00  \n",
       "451        0.000000e+00  \n",
       "452        0.000000e+00  \n",
       "453        0.000000e+00  \n",
       "454        0.000000e+00  \n",
       "455        0.000000e+00  \n",
       "456        0.000000e+00  \n",
       "457        0.000000e+00  \n",
       "458        0.000000e+00  \n",
       "459        0.000000e+00  \n",
       "460        0.000000e+00  \n",
       "461        0.000000e+00  \n",
       "462        0.000000e+00  \n",
       "463        0.000000e+00  "
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "poly_feat_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-running the model removing non-important features\n",
    "poly_feat_importance = list(poly_feat_perf.features[:71])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_features_poly = df_poly2.loc[:,poly_feat_importance]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conduct Train Test split on new feature set\n",
    "X_train_poly_fin, X_test_poly_fin, y_train_poly_fin, y_test_poly_fin = train_test_split(final_features_poly, df_target, \n",
    "                                                                    random_state=1, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    1.9s\n",
      "[Parallel(n_jobs=-1)]: Done 280 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=-1)]: Done 780 tasks      | elapsed:   12.6s\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:   18.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=1),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5, 10, 15, 20],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [5, 7, 10]},\n",
       "             scoring='f1', verbose=1)"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the instance\n",
    "fin_poly_grid = GridSearchCV(clf, dt_params, cv=10, scoring='f1', verbose=1, n_jobs=-1)\n",
    "\n",
    "# Fit the decision tree classifier to the training data\n",
    "fin_poly_grid.fit(X_train_poly_fin, y_train_poly_fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions for y values in the training set\n",
    "finpoly_train_preds = fin_poly_grid.best_estimator_.predict(X_train_poly_fin)\n",
    "\n",
    "# Make predictions for y values in the testing dataset\n",
    "finpoly_test_preds = fin_poly_grid.best_estimator_.predict(X_test_poly_fin)\n",
    "\n",
    "# Train Metrics\n",
    "finpoly_train_accuracy_temp = metrics.accuracy_score(y_train_poly_fin, finpoly_train_preds)\n",
    "finpoly_train_precision_temp = metrics.precision_score(y_train_poly_fin, finpoly_train_preds)\n",
    "finpoly_train_recall_temp = metrics.recall_score(y_train_poly_fin, finpoly_train_preds)\n",
    "finpoly_train_f1_temp = metrics.f1_score(y_train_poly_fin, finpoly_train_preds)\n",
    "\n",
    "# Test Metrics\n",
    "finpoly_accuracy_temp = metrics.accuracy_score(y_test_poly_fin, finpoly_test_preds)\n",
    "finpoly_precision_temp = metrics.precision_score(y_test_poly_fin, finpoly_test_preds)\n",
    "finpoly_recall_temp = metrics.recall_score(y_test_poly_fin, finpoly_test_preds)\n",
    "finpoly_f1_temp = metrics.f1_score(y_test_poly_fin, finpoly_test_preds)\n",
    "\n",
    "\n",
    "performance.append({'model': 'Poly GridSearchCV Decision Tree Model w/ Feature Importance', '# Features': X_train_poly_fin.shape[1], 'Train Accuracy': finpoly_train_accuracy_temp,\n",
    "                    'Test Accuracy': finpoly_accuracy_temp, 'Train Recall': finpoly_train_recall_temp,\n",
    "                    'Test Recall': finpoly_recall_temp, 'Train Precision': finpoly_train_precision_temp,\n",
    "                    'Test Precision': finpoly_precision_temp, 'Train F1': finpoly_train_f1_temp,\n",
    "                    'Test F1': finpoly_f1_temp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "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>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Feature Im...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>0.869811</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.737190</td>\n",
       "      <td>0.787415</td>\n",
       "      <td>0.731148</td>\n",
       "      <td>0.781599</td>\n",
       "      <td>0.734156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Basic LogReg w/ Final Features &amp; RFECV</td>\n",
       "      <td>29</td>\n",
       "      <td>0.858007</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>0.772937</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.680310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.723671</td>\n",
       "      <td>0.718964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Final Feat...</td>\n",
       "      <td>29</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Final GridSearchCV Decision Tree Model w/ Feat...</td>\n",
       "      <td>24</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Poly Features</td>\n",
       "      <td>464</td>\n",
       "      <td>0.887937</td>\n",
       "      <td>0.865377</td>\n",
       "      <td>0.782572</td>\n",
       "      <td>0.752066</td>\n",
       "      <td>0.759041</td>\n",
       "      <td>0.712050</td>\n",
       "      <td>0.770627</td>\n",
       "      <td>0.731511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Poly GridSearchCV Decision Tree Model w/ Featu...</td>\n",
       "      <td>71</td>\n",
       "      <td>0.888340</td>\n",
       "      <td>0.864571</td>\n",
       "      <td>0.783829</td>\n",
       "      <td>0.752066</td>\n",
       "      <td>0.759643</td>\n",
       "      <td>0.709828</td>\n",
       "      <td>0.771546</td>\n",
       "      <td>0.730337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model  # Features  \\\n",
       "0                           Basic Logistic Regression         231   \n",
       "1                    GridSearchCV Logistic Regression         231   \n",
       "2                  Basic Logistic Regression Selected          99   \n",
       "3           Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                           Basic Decision Tree Model         231   \n",
       "5                    GridSearchCV Decision Tree Model         231   \n",
       "6               Basic Decision Tree Selected Features          99   \n",
       "7   GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "8   GridSearchCV Decision Tree Model w/ Feature Im...          66   \n",
       "9              Basic LogReg w/ Final Features & RFECV          29   \n",
       "10  GridSearchCV Decision Tree Model w/ Final Feat...          29   \n",
       "11  Final GridSearchCV Decision Tree Model w/ Feat...          24   \n",
       "12  GridSearchCV Decision Tree Model w/ Poly Features         464   \n",
       "13  Poly GridSearchCV Decision Tree Model w/ Featu...          71   \n",
       "\n",
       "    Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0         0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1         0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2         0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3         0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4         1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5         0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6         1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7         0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "8         0.895697       0.869811      0.775869     0.737190         0.787415   \n",
       "9         0.858007       0.851270      0.772937     0.780165         0.680310   \n",
       "10        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "11        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "12        0.887937       0.865377      0.782572     0.752066         0.759041   \n",
       "13        0.888340       0.864571      0.783829     0.752066         0.759643   \n",
       "\n",
       "    Test Precision  Train F1   Test F1  \n",
       "0         0.678873  0.736012  0.733080  \n",
       "1         0.674221  0.730911  0.726163  \n",
       "2         0.680851  0.730984  0.732824  \n",
       "3         0.696165  0.724406  0.735776  \n",
       "4         0.692810  1.000000  0.696795  \n",
       "5         0.745424  0.803183  0.742952  \n",
       "6         0.660743  1.000000  0.668301  \n",
       "7         0.727124  0.780190  0.731306  \n",
       "8         0.731148  0.781599  0.734156  \n",
       "9         0.666667  0.723671  0.718964  \n",
       "10        0.741408  0.762773  0.745066  \n",
       "11        0.741408  0.762773  0.745066  \n",
       "12        0.712050  0.770627  0.731511  \n",
       "13        0.709828  0.771546  0.730337  "
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination with Logistic Regression\n",
    "As of now, the most recent model decreased in performance, but what if we use RFECV to select the optimal number of features and take the top 15 again from grid search?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-345-408c508c051c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mselector_poly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRFECV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mselector_poly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_poly\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_poly\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_selection/_rfe.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    548\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_rfe_single_fit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m         scores = parallel(\n\u001b[0m\u001b[1;32m    551\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrfe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             for train, test in cv.split(X, y, groups))\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_selection/_rfe.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m         scores = parallel(\n\u001b[0;32m--> 551\u001b[0;31m             \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrfe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             for train, test in cv.split(X, y, groups))\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_selection/_rfe.py\u001b[0m in \u001b[0;36m_rfe_single_fit\u001b[0;34m(rfe, estimator, X, y, train, test, scorer)\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_safe_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_safe_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m     return rfe._fit(\n\u001b[0m\u001b[1;32m     34\u001b[0m         \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         _score(estimator, X_test[:, features], y_test, scorer)).scores_\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/feature_selection/_rfe.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, step_score)\u001b[0m\n\u001b[1;32m    194\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Fitting estimator with %d features.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msupport_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0;31m# Get coefs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1405\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m             \u001b[0mprefer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'processes'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n\u001b[0m\u001b[1;32m   1408\u001b[0m                                \u001b[0;34m**\u001b[0m\u001b[0m_joblib_parallel_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprefer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m             path_func(X, y, pos_class=class_, Cs=[C_],\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1027\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    845\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    846\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 847\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    848\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    763\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 765\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    766\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    253\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    253\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36m_logistic_regression_path\u001b[0;34m(X, y, pos_class, Cs, fit_intercept, max_iter, tol, verbose, solver, coef, class_weight, dual, penalty, intercept_scaling, multi_class, random_state, check_input, max_squared_sum, sample_weight, l1_ratio)\u001b[0m\n\u001b[1;32m    755\u001b[0m             iprint = [-1, 50, 1, 100, 101][\n\u001b[1;32m    756\u001b[0m                 np.searchsorted(np.array([0, 1, 2, 3]), verbose)]\n\u001b[0;32m--> 757\u001b[0;31m             opt_res = optimize.minimize(\n\u001b[0m\u001b[1;32m    758\u001b[0m                 \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"L-BFGS-B\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    615\u001b[0m                                   **options)\n\u001b[1;32m    616\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'l-bfgs-b'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 617\u001b[0;31m         return _minimize_lbfgsb(fun, x0, args, jac, bounds,\n\u001b[0m\u001b[1;32m    618\u001b[0m                                 callback=callback, **options)\n\u001b[1;32m    619\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tnc'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[0;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[1;32m    358\u001b[0m             \u001b[0;31m# until the completion of the current minimization iteration.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m             \u001b[0;31m# Overwrite f and g:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m             \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    361\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb'NEW_X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m             \u001b[0;31m# new iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mfun_and_grad\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_x_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36m_update_fun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_update_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_updated\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_updated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mupdate_fun\u001b[0;34m()\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun_impl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mfun_wrapped\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfun_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnfev\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;34m\"\"\" returns the the function value \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_if_needed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m_compute_if_needed\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m             \u001b[0mfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36m_logistic_loss_and_grad\u001b[0;34m(w, X, y, alpha, sample_weight)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0mz0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m     \u001b[0mgrad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn_features\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msafe_sparse_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;31m# Case where we fit the intercept.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m     if (sparse.issparse(a) and sparse.issparse(b)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "selector_poly = RFECV(LogisticRegression(class_weight='balanced', max_iter=10000), step=1, cv=10)\n",
    "selector_poly.fit(X_train_poly, y_train_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th># Features</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Train Recall</th>\n",
       "      <th>Test Recall</th>\n",
       "      <th>Train Precision</th>\n",
       "      <th>Test Precision</th>\n",
       "      <th>Train F1</th>\n",
       "      <th>Test F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.861635</td>\n",
       "      <td>0.858525</td>\n",
       "      <td>0.801843</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.680171</td>\n",
       "      <td>0.678873</td>\n",
       "      <td>0.736012</td>\n",
       "      <td>0.733080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GridSearchCV Logistic Regression</td>\n",
       "      <td>231</td>\n",
       "      <td>0.860425</td>\n",
       "      <td>0.855300</td>\n",
       "      <td>0.788018</td>\n",
       "      <td>0.796694</td>\n",
       "      <td>0.681522</td>\n",
       "      <td>0.674221</td>\n",
       "      <td>0.730911</td>\n",
       "      <td>0.726163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic Logistic Regression Selected</td>\n",
       "      <td>99</td>\n",
       "      <td>0.859216</td>\n",
       "      <td>0.858928</td>\n",
       "      <td>0.795140</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.676408</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>0.732824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic LogReg w/ Selected Features &amp; RFECV</td>\n",
       "      <td>15</td>\n",
       "      <td>0.860828</td>\n",
       "      <td>0.863362</td>\n",
       "      <td>0.760369</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.691692</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>0.724406</td>\n",
       "      <td>0.735776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GridSearchCV Decision Tree Model</td>\n",
       "      <td>231</td>\n",
       "      <td>0.905271</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.803519</td>\n",
       "      <td>0.740496</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.745424</td>\n",
       "      <td>0.803183</td>\n",
       "      <td>0.742952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic Decision Tree Selected Features</td>\n",
       "      <td>99</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Selected F...</td>\n",
       "      <td>99</td>\n",
       "      <td>0.894891</td>\n",
       "      <td>0.868198</td>\n",
       "      <td>0.775450</td>\n",
       "      <td>0.735537</td>\n",
       "      <td>0.784987</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>0.731306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Feature Im...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.895697</td>\n",
       "      <td>0.869811</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.737190</td>\n",
       "      <td>0.787415</td>\n",
       "      <td>0.731148</td>\n",
       "      <td>0.781599</td>\n",
       "      <td>0.734156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Basic LogReg w/ Final Features &amp; RFECV</td>\n",
       "      <td>29</td>\n",
       "      <td>0.858007</td>\n",
       "      <td>0.851270</td>\n",
       "      <td>0.772937</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>0.680310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.723671</td>\n",
       "      <td>0.718964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Final Feat...</td>\n",
       "      <td>29</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Final GridSearchCV Decision Tree Model w/ Feat...</td>\n",
       "      <td>24</td>\n",
       "      <td>0.887232</td>\n",
       "      <td>0.875050</td>\n",
       "      <td>0.753666</td>\n",
       "      <td>0.748760</td>\n",
       "      <td>0.772103</td>\n",
       "      <td>0.741408</td>\n",
       "      <td>0.762773</td>\n",
       "      <td>0.745066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GridSearchCV Decision Tree Model w/ Poly Features</td>\n",
       "      <td>464</td>\n",
       "      <td>0.887937</td>\n",
       "      <td>0.865377</td>\n",
       "      <td>0.782572</td>\n",
       "      <td>0.752066</td>\n",
       "      <td>0.759041</td>\n",
       "      <td>0.712050</td>\n",
       "      <td>0.770627</td>\n",
       "      <td>0.731511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Poly GridSearchCV Decision Tree Model w/ Featu...</td>\n",
       "      <td>71</td>\n",
       "      <td>0.888340</td>\n",
       "      <td>0.864571</td>\n",
       "      <td>0.783829</td>\n",
       "      <td>0.752066</td>\n",
       "      <td>0.759643</td>\n",
       "      <td>0.709828</td>\n",
       "      <td>0.771546</td>\n",
       "      <td>0.730337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                model  # Features  \\\n",
       "0                           Basic Logistic Regression         231   \n",
       "1                    GridSearchCV Logistic Regression         231   \n",
       "2                  Basic Logistic Regression Selected          99   \n",
       "3           Basic LogReg w/ Selected Features & RFECV          15   \n",
       "4                           Basic Decision Tree Model         231   \n",
       "5                    GridSearchCV Decision Tree Model         231   \n",
       "6               Basic Decision Tree Selected Features          99   \n",
       "7   GridSearchCV Decision Tree Model w/ Selected F...          99   \n",
       "8   GridSearchCV Decision Tree Model w/ Feature Im...          66   \n",
       "9              Basic LogReg w/ Final Features & RFECV          29   \n",
       "10  GridSearchCV Decision Tree Model w/ Final Feat...          29   \n",
       "11  Final GridSearchCV Decision Tree Model w/ Feat...          24   \n",
       "12  GridSearchCV Decision Tree Model w/ Poly Features         464   \n",
       "13  Poly GridSearchCV Decision Tree Model w/ Featu...          71   \n",
       "\n",
       "    Train Accuracy  Test Accuracy  Train Recall  Test Recall  Train Precision  \\\n",
       "0         0.861635       0.858525      0.801843     0.796694         0.680171   \n",
       "1         0.860425       0.855300      0.788018     0.796694         0.681522   \n",
       "2         0.859216       0.858928      0.795140     0.793388         0.676408   \n",
       "3         0.860828       0.863362      0.760369     0.780165         0.691692   \n",
       "4         1.000000       0.851270      1.000000     0.700826         1.000000   \n",
       "5         0.905271       0.875050      0.803519     0.740496         0.802846   \n",
       "6         1.000000       0.836356      1.000000     0.676033         1.000000   \n",
       "7         0.894891       0.868198      0.775450     0.735537         0.784987   \n",
       "8         0.895697       0.869811      0.775869     0.737190         0.787415   \n",
       "9         0.858007       0.851270      0.772937     0.780165         0.680310   \n",
       "10        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "11        0.887232       0.875050      0.753666     0.748760         0.772103   \n",
       "12        0.887937       0.865377      0.782572     0.752066         0.759041   \n",
       "13        0.888340       0.864571      0.783829     0.752066         0.759643   \n",
       "\n",
       "    Test Precision  Train F1   Test F1  \n",
       "0         0.678873  0.736012  0.733080  \n",
       "1         0.674221  0.730911  0.726163  \n",
       "2         0.680851  0.730984  0.732824  \n",
       "3         0.696165  0.724406  0.735776  \n",
       "4         0.692810  1.000000  0.696795  \n",
       "5         0.745424  0.803183  0.742952  \n",
       "6         0.660743  1.000000  0.668301  \n",
       "7         0.727124  0.780190  0.731306  \n",
       "8         0.731148  0.781599  0.734156  \n",
       "9         0.666667  0.723671  0.718964  \n",
       "10        0.741408  0.762773  0.745066  \n",
       "11        0.741408  0.762773  0.745066  \n",
       "12        0.712050  0.770627  0.731511  \n",
       "13        0.709828  0.771546  0.730337  "
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(performance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to lack of time, I will not be able to run an RFECV model, so I'll choose the best performing model from above. \n",
    "\n",
    "**GridSearch Decision Tree Model with Best Performing Features and Non-zero feature importance (11)**\n",
    "This modelruns the best on 24 features for enhanced interpretability. This model performs the best and most consistent across all metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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