chapter-06-trees/06-trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source https://github.com/gastonstat/CreditScoring\n",
    "\n",
    "To download the dataset, run\n",
    "\n",
    "```\n",
    "wget https://github.com/gastonstat/CreditScoring/raw/master/CreditScoring.csv\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Status\",\"Seniority\",\"Home\",\"Time\",\"Age\",\"Marital\",\"Records\",\"Job\",\"Expenses\",\"Income\",\"Assets\",\"Debt\",\"Amount\",\"Price\"\r\n",
      "1,9,1,60,30,2,1,3,73,129,0,0,800,846\r\n",
      "1,17,1,60,58,3,1,1,48,131,0,0,1000,1658\r\n",
      "2,10,2,36,46,2,2,3,90,200,3000,0,2000,2985\r\n",
      "1,0,1,60,24,1,1,1,63,182,2500,0,900,1325\r\n",
      "1,0,1,36,26,1,1,1,46,107,0,0,310,910\r\n",
      "1,1,2,60,36,2,1,1,75,214,3500,0,650,1645\r\n",
      "1,29,2,60,44,2,1,1,75,125,10000,0,1600,1800\r\n",
      "1,9,5,12,27,1,1,1,35,80,0,0,200,1093\r\n",
      "1,0,2,60,32,2,1,3,90,107,15000,0,1200,1957\r\n"
     ]
    }
   ],
   "source": [
    "!head CreditScoring.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('CreditScoring.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Status</th>\n",
       "      <th>Seniority</th>\n",
       "      <th>Home</th>\n",
       "      <th>Time</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital</th>\n",
       "      <th>Records</th>\n",
       "      <th>Job</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>Income</th>\n",
       "      <th>Assets</th>\n",
       "      <th>Debt</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "      <td>30</td>\n",
       "      <td>2</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1658</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>182</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>1325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>310</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Status  Seniority  Home  Time  Age  Marital  Records  Job  Expenses  \\\n",
       "0       1          9     1    60   30        2        1    3        73   \n",
       "1       1         17     1    60   58        3        1    1        48   \n",
       "2       2         10     2    36   46        2        2    3        90   \n",
       "3       1          0     1    60   24        1        1    1        63   \n",
       "4       1          0     1    36   26        1        1    1        46   \n",
       "\n",
       "   Income  Assets  Debt  Amount  Price  \n",
       "0     129       0     0     800    846  \n",
       "1     131       0     0    1000   1658  \n",
       "2     200    3000     0    2000   2985  \n",
       "3     182    2500     0     900   1325  \n",
       "4     107       0     0     310    910  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>records</th>\n",
       "      <th>job</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
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       "      <td>9</td>\n",
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       "      <td>73</td>\n",
       "      <td>129</td>\n",
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       "      <td>0</td>\n",
       "      <td>800</td>\n",
       "      <td>846</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>200</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>182</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>1325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>310</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   status  seniority  home  time  age  marital  records  job  expenses  \\\n",
       "0       1          9     1    60   30        2        1    3        73   \n",
       "1       1         17     1    60   58        3        1    1        48   \n",
       "2       2         10     2    36   46        2        2    3        90   \n",
       "3       1          0     1    60   24        1        1    1        63   \n",
       "4       1          0     1    36   26        1        1    1        46   \n",
       "\n",
       "   income  assets  debt  amount  price  \n",
       "0     129       0     0     800    846  \n",
       "1     131       0     0    1000   1658  \n",
       "2     200    3000     0    2000   2985  \n",
       "3     182    2500     0     900   1325  \n",
       "4     107       0     0     310    910  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.lower()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical variables are represented by numbers. For converting, we'll use the following map (extracted from [Part1_CredScoring_Processing.R](https://github.com/gastonstat/CreditScoring/blob/master/Part1_CredScoring_Processing.R)). It's code in R which we'll translate to Pandas.\n",
    "\n",
    "```\n",
    "levels(dd$Status) = c(\"good\", \"bad\")\n",
    "levels(dd$Home) = c(\"rent\", \"owner\", \"priv\", \"ignore\", \"parents\", \"other\")\n",
    "levels(dd$Marital) = c(\"single\", \"married\", \"widow\", \"separated\", \"divorced\")\n",
    "levels(dd$Records) = c(\"no_rec\", \"yes_rec\")\n",
    "levels(dd$Job) = c(\"fixed\", \"partime\", \"freelance\", \"others\")\n",
    "```\n",
    "\n",
    "Let's convert it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>records</th>\n",
       "      <th>job</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ok</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>800</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ok</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>131</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1658</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>default</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>182</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>1325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>310</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    status  seniority  home  time  age  marital  records  job  expenses  \\\n",
       "0       ok          9     1    60   30        2        1    3        73   \n",
       "1       ok         17     1    60   58        3        1    1        48   \n",
       "2  default         10     2    36   46        2        2    3        90   \n",
       "3       ok          0     1    60   24        1        1    1        63   \n",
       "4       ok          0     1    36   26        1        1    1        46   \n",
       "\n",
       "   income  assets  debt  amount  price  \n",
       "0     129       0     0     800    846  \n",
       "1     131       0     0    1000   1658  \n",
       "2     200    3000     0    2000   2985  \n",
       "3     182    2500     0     900   1325  \n",
       "4     107       0     0     310    910  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status_values = {\n",
    "    1: 'ok',\n",
    "    2: 'default',\n",
    "    0: 'unk'\n",
    "}\n",
    "\n",
    "df.status = df.status.map(status_values)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_values = {\n",
    "    1: 'rent',\n",
    "    2: 'owner',\n",
    "    3: 'private',\n",
    "    4: 'ignore',\n",
    "    5: 'parents',\n",
    "    6: 'other',\n",
    "    0: 'unk'\n",
    "}\n",
    "\n",
    "df.home = df.home.map(home_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "marital_values = {\n",
    "    1: 'single',\n",
    "    2: 'married',\n",
    "    3: 'widow',\n",
    "    4: 'separated',\n",
    "    5: 'divorced',\n",
    "    0: 'unk'\n",
    "}\n",
    "\n",
    "df.marital = df.marital.map(marital_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "records_values = {\n",
    "    1: 'no',\n",
    "    2: 'yes',\n",
    "    0: 'unk'\n",
    "}\n",
    "\n",
    "df.records = df.records.map(records_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_values = {\n",
    "    1: 'fixed',\n",
    "    2: 'partime',\n",
    "    3: 'freelance',\n",
    "    4: 'others',\n",
    "    0: 'unk'\n",
    "}\n",
    "\n",
    "df.job = df.job.map(job_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>status</th>\n",
       "      <th>seniority</th>\n",
       "      <th>home</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>records</th>\n",
       "      <th>job</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ok</td>\n",
       "      <td>9</td>\n",
       "      <td>rent</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>freelance</td>\n",
       "      <td>73</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>800</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ok</td>\n",
       "      <td>17</td>\n",
       "      <td>rent</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>widow</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>48</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>default</td>\n",
       "      <td>10</td>\n",
       "      <td>owner</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>freelance</td>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>rent</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>63</td>\n",
       "      <td>182</td>\n",
       "      <td>2500</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>1325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ok</td>\n",
       "      <td>0</td>\n",
       "      <td>rent</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>fixed</td>\n",
       "      <td>46</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>310</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    status  seniority   home  time  age  marital records        job  expenses  \\\n",
       "0       ok          9   rent    60   30  married      no  freelance        73   \n",
       "1       ok         17   rent    60   58    widow      no      fixed        48   \n",
       "2  default         10  owner    36   46  married     yes  freelance        90   \n",
       "3       ok          0   rent    60   24   single      no      fixed        63   \n",
       "4       ok          0   rent    36   26   single      no      fixed        46   \n",
       "\n",
       "   income  assets  debt  amount  price  \n",
       "0     129       0     0     800    846  \n",
       "1     131       0     0    1000   1658  \n",
       "2     200    3000     0    2000   2985  \n",
       "3     182    2500     0     900   1325  \n",
       "4     107       0     0     310    910  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this, all the categorical variables have strings, not numbers\n",
    "\n",
    "Let's look at numberical variables:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seniority</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>763317.0</td>\n",
       "      <td>1060341.0</td>\n",
       "      <td>404382.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>1463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8703625.0</td>\n",
       "      <td>10217569.0</td>\n",
       "      <td>6344253.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seniority    time     age  expenses      income      assets  \\\n",
       "count     4455.0  4455.0  4455.0    4455.0      4455.0      4455.0   \n",
       "mean         8.0    46.0    37.0      56.0    763317.0   1060341.0   \n",
       "std          8.0    15.0    11.0      20.0   8703625.0  10217569.0   \n",
       "min          0.0     6.0    18.0      35.0         0.0         0.0   \n",
       "25%          2.0    36.0    28.0      35.0        80.0         0.0   \n",
       "50%          5.0    48.0    36.0      51.0       120.0      3500.0   \n",
       "75%         12.0    60.0    45.0      72.0       166.0      6000.0   \n",
       "max         48.0    72.0    68.0     180.0  99999999.0  99999999.0   \n",
       "\n",
       "             debt  amount    price  \n",
       "count      4455.0  4455.0   4455.0  \n",
       "mean     404382.0  1039.0   1463.0  \n",
       "std     6344253.0   475.0    628.0  \n",
       "min           0.0   100.0    105.0  \n",
       "25%           0.0   700.0   1118.0  \n",
       "50%           0.0  1000.0   1400.0  \n",
       "75%           0.0  1300.0   1692.0  \n",
       "max    99999999.0  5000.0  11140.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "99999999 indicate missing values. Let's replace it by `NaN`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in ['income', 'assets', 'debt']:\n",
    "    df[c] = df[c].replace(to_replace=99999999, value=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status        0\n",
       "seniority     0\n",
       "home          0\n",
       "time          0\n",
       "age           0\n",
       "marital       0\n",
       "records       0\n",
       "job           0\n",
       "expenses      0\n",
       "income       34\n",
       "assets       47\n",
       "debt         18\n",
       "amount        0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the stats are more meaninful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>seniority</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>expenses</th>\n",
       "      <th>income</th>\n",
       "      <th>assets</th>\n",
       "      <th>debt</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>4455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>5403.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>1463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>11573.0</td>\n",
       "      <td>1246.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>959.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seniority    time     age  expenses  income    assets     debt  amount  \\\n",
       "count     4455.0  4455.0  4455.0    4455.0  4421.0    4408.0   4437.0  4455.0   \n",
       "mean         8.0    46.0    37.0      56.0   131.0    5403.0    343.0  1039.0   \n",
       "std          8.0    15.0    11.0      20.0    86.0   11573.0   1246.0   475.0   \n",
       "min          0.0     6.0    18.0      35.0     0.0       0.0      0.0   100.0   \n",
       "25%          2.0    36.0    28.0      35.0    80.0       0.0      0.0   700.0   \n",
       "50%          5.0    48.0    36.0      51.0   120.0    3000.0      0.0  1000.0   \n",
       "75%         12.0    60.0    45.0      72.0   165.0    6000.0      0.0  1300.0   \n",
       "max         48.0    72.0    68.0     180.0   959.0  300000.0  30000.0  5000.0   \n",
       "\n",
       "         price  \n",
       "count   4455.0  \n",
       "mean    1463.0  \n",
       "std      628.0  \n",
       "min      105.0  \n",
       "25%     1118.0  \n",
       "50%     1400.0  \n",
       "75%     1692.0  \n",
       "max    11140.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ok         3200\n",
       "default    1254\n",
       "unk           1\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's one record for which the status is unknown. Let's remove it - it's not useful for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.status != 'unk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status        0\n",
       "seniority     0\n",
       "home          0\n",
       "time          0\n",
       "age           0\n",
       "marital       0\n",
       "records       0\n",
       "job           0\n",
       "expenses      0\n",
       "income       34\n",
       "assets       47\n",
       "debt         18\n",
       "amount        0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to prepare the data for training:\n",
    "\n",
    "* First, do train-validation-test split\n",
    "* Then, apply one-hot encoding to categorical features and get the feature matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_full, df_test = train_test_split(df, test_size=0.2, random_state=11)\n",
    "df_train, df_val = train_test_split(df_train_full, test_size=0.25, random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = (df_train.status == 'default').values\n",
    "y_val = (df_val.status == 'default').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_train['status']\n",
    "del df_val['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2672, 891, 891)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train), len(df_val), len(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For OHE, we'll use `DictVectorizer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember that we have some missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seniority     0\n",
       "home          0\n",
       "time          0\n",
       "age           0\n",
       "marital       0\n",
       "records       0\n",
       "job           0\n",
       "expenses      0\n",
       "income       25\n",
       "assets       30\n",
       "debt         11\n",
       "amount        0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll replace them with \"0\" like in Chapter 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_train = df_train.fillna(0).to_dict(orient='records')\n",
    "dict_val = df_val.fillna(0).to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'seniority': 10,\n",
       " 'home': 'owner',\n",
       " 'time': 36,\n",
       " 'age': 36,\n",
       " 'marital': 'married',\n",
       " 'records': 'no',\n",
       " 'job': 'freelance',\n",
       " 'expenses': 75,\n",
       " 'income': 0.0,\n",
       " 'assets': 10000.0,\n",
       " 'debt': 0.0,\n",
       " 'amount': 1000,\n",
       " 'price': 1400}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "\n",
    "X_train = dv.fit_transform(dict_train)\n",
    "X_val = dv.transform(dict_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to train a model. We'll start with decision trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision trees\n",
    "\n",
    "We'll use `DecisionTreeClassifier` and for evaluating the quality of our models, we'll use AUC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit the tree with default parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the predictions (probabilities), we use `predict_proba`. Let's check AUC on train data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dt.predict_proba(X_train)[:, 1]\n",
    "roc_auc_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And on validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6562537841176043"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a case of _overfitting_ - our model on the training data performs perfectly, but fails on validation\n",
    "\n",
    "Let's change the depth parameter: restring the size of the tree to 2 levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train auc: 0.705\n",
      "val auc: 0.669\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=2)\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred = dt.predict_proba(X_train)[:, 1]\n",
    "auc = roc_auc_score(y_train, y_pred)\n",
    "print('train auc: %.3f' % auc)\n",
    "\n",
    "y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "auc = roc_auc_score(y_val, y_pred)\n",
    "print('val auc: %.3f' % auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's even better than the previous tree!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=6)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=6)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dt.predict_proba(X_val)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7562392531060037"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1 -> 0.606\n",
      "   2 -> 0.669\n",
      "   3 -> 0.739\n",
      "   4 -> 0.761\n",
      "   5 -> 0.766\n",
      "   6 -> 0.758\n",
      "  10 -> 0.695\n",
      "  15 -> 0.661\n",
      "  20 -> 0.653\n",
      "None -> 0.651\n"
     ]
    }
   ],
   "source": [
    "for depth in [1, 2, 3, 4, 5, 6, 10, 15, 20, None]:\n",
    "    dt = DecisionTreeClassifier(max_depth=depth)\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    print('%4s -> %.3f' % (depth, auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -> 0.761\n",
      "5 -> 0.761\n",
      "10 -> 0.761\n",
      "15 -> 0.764\n",
      "20 -> 0.761\n",
      "50 -> 0.753\n",
      "100 -> 0.756\n",
      "200 -> 0.747\n"
     ]
    }
   ],
   "source": [
    "for m in [1, 5, 10, 15, 20, 50, 100, 200]:\n",
    "    dt = DecisionTreeClassifier(max_depth=4, min_samples_leaf=m)\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    print('%s -> %.3f' % (m, auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth: 4\n",
      "1 -> 0.761\n",
      "5 -> 0.761\n",
      "10 -> 0.761\n",
      "15 -> 0.764\n",
      "20 -> 0.761\n",
      "50 -> 0.753\n",
      "100 -> 0.756\n",
      "200 -> 0.747\n",
      "\n",
      "depth: 5\n",
      "1 -> 0.766\n",
      "5 -> 0.768\n",
      "10 -> 0.762\n",
      "15 -> 0.772\n",
      "20 -> 0.774\n",
      "50 -> 0.768\n",
      "100 -> 0.763\n",
      "200 -> 0.759\n",
      "\n",
      "depth: 6\n",
      "1 -> 0.757\n",
      "5 -> 0.762\n",
      "10 -> 0.778\n",
      "15 -> 0.785\n",
      "20 -> 0.774\n",
      "50 -> 0.772\n",
      "100 -> 0.776\n",
      "200 -> 0.768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for m in [4, 5, 6]:\n",
    "    print('depth: %s' % m)\n",
    "\n",
    "    for s in [1, 5, 10, 15, 20, 50, 100, 200]:\n",
    "        dt = DecisionTreeClassifier(max_depth=m, min_samples_leaf=s)\n",
    "        dt.fit(X_train, y_train)\n",
    "        y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "        auc = roc_auc_score(y_val, y_pred)\n",
    "        print('%s -> %.3f' % (s, auc))\n",
    "    \n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -> 0.744\n",
      "5 -> 0.759\n",
      "10 -> 0.778\n",
      "15 -> 0.785\n",
      "20 -> 0.774\n",
      "50 -> 0.770\n",
      "100 -> 0.776\n",
      "200 -> 0.768\n"
     ]
    }
   ],
   "source": [
    "for m in [1, 5, 10, 15, 20, 50, 100, 200]:\n",
    "    dt = DecisionTreeClassifier(max_depth=6, min_samples_leaf=m)\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred = dt.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    print('%s -> %.3f' % (m, auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7854738320699426"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=6, min_samples_leaf=15)\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred_dt = dt.predict_proba(X_val)[:, 1]\n",
    "roc_auc_score(y_val, y_pred_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_val, y_pred_dt)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "plt.plot(fpr, tpr, color='black')\n",
    "plt.plot([0, 1], [0, 1], color='black', lw=0.7, linestyle='dashed', alpha=0.5)\n",
    "\n",
    "plt.xlim([-0.02, 1.02])\n",
    "plt.ylim([-0.02, 1.02])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.title('ROC curve')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7810782010607639"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we repeat one more time - it changes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7797673827226272"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand how much it changes, we can repeat it 100 times and look at the mean and the standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.784 +- 0.010\n",
      "0.774 -- 0.793\n"
     ]
    }
   ],
   "source": [
    "aucs = []\n",
    "\n",
    "for i in range(100):\n",
    "    rf = RandomForestClassifier(n_estimators=10)\n",
    "    rf.fit(X_train, y_train)\n",
    "    y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    aucs.append(auc)\n",
    "\n",
    "auc_mean = np.mean(aucs)\n",
    "auc_std = np.std(aucs)\n",
    "\n",
    "print('%.3f +- %.3f' % (auc_mean, auc_std))\n",
    "print('%.3f -- %.3f' % (auc_mean -  auc_std, auc_mean + auc_std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fix this issue, let's set the seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7811145285897654"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10, random_state=3)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll check how AUC depends on the number of trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 -> 0.781\n",
      "20 -> 0.801\n",
      "30 -> 0.813\n",
      "40 -> 0.815\n",
      "50 -> 0.816\n",
      "60 -> 0.817\n",
      "70 -> 0.818\n",
      "80 -> 0.818\n",
      "90 -> 0.820\n",
      "100 -> 0.819\n",
      "110 -> 0.819\n",
      "120 -> 0.821\n",
      "130 -> 0.822\n",
      "140 -> 0.822\n",
      "150 -> 0.820\n",
      "160 -> 0.820\n",
      "170 -> 0.819\n",
      "180 -> 0.820\n",
      "190 -> 0.820\n",
      "200 -> 0.820\n"
     ]
    }
   ],
   "source": [
    "aucs = []\n",
    "\n",
    "for i in range(10, 201, 10):\n",
    "    rf = RandomForestClassifier(n_estimators=i, random_state=3)\n",
    "    rf.fit(X_train, y_train)\n",
    "    y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    print('%s -> %.3f' % (i, auc))\n",
    "    aucs.append(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(range(10, 201, 10), aucs, color='black')\n",
    "plt.xticks(range(0, 201, 50))\n",
    "\n",
    "plt.title('Number of trees vs AUC')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_random_forest_n_estimators.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuninig the `max_depth` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth: 5\n",
      "10 -> 0.788\n",
      "20 -> 0.798\n",
      "30 -> 0.800\n",
      "40 -> 0.800\n",
      "50 -> 0.800\n",
      "60 -> 0.801\n",
      "70 -> 0.802\n",
      "80 -> 0.803\n",
      "90 -> 0.804\n",
      "100 -> 0.804\n",
      "110 -> 0.807\n",
      "120 -> 0.806\n",
      "130 -> 0.808\n",
      "140 -> 0.807\n",
      "150 -> 0.807\n",
      "160 -> 0.807\n",
      "170 -> 0.807\n",
      "180 -> 0.807\n",
      "190 -> 0.807\n",
      "200 -> 0.807\n",
      "\n",
      "depth: 10\n",
      "10 -> 0.791\n",
      "20 -> 0.808\n",
      "30 -> 0.812\n",
      "40 -> 0.818\n",
      "50 -> 0.817\n",
      "60 -> 0.819\n",
      "70 -> 0.820\n",
      "80 -> 0.820\n",
      "90 -> 0.820\n",
      "100 -> 0.821\n",
      "110 -> 0.823\n",
      "120 -> 0.823\n",
      "130 -> 0.825\n",
      "140 -> 0.824\n",
      "150 -> 0.824\n",
      "160 -> 0.825\n",
      "170 -> 0.825\n",
      "180 -> 0.825\n",
      "190 -> 0.825\n",
      "200 -> 0.825\n",
      "\n",
      "depth: 20\n",
      "10 -> 0.776\n",
      "20 -> 0.803\n",
      "30 -> 0.813\n",
      "40 -> 0.817\n",
      "50 -> 0.815\n",
      "60 -> 0.813\n",
      "70 -> 0.814\n",
      "80 -> 0.815\n",
      "90 -> 0.814\n",
      "100 -> 0.815\n",
      "110 -> 0.817\n",
      "120 -> 0.816\n",
      "130 -> 0.818\n",
      "140 -> 0.818\n",
      "150 -> 0.818\n",
      "160 -> 0.818\n",
      "170 -> 0.817\n",
      "180 -> 0.817\n",
      "190 -> 0.817\n",
      "200 -> 0.818\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_aucs = {}\n",
    "\n",
    "for depth in [5, 10, 20]:\n",
    "    print('depth: %s' % depth)\n",
    "    aucs = []\n",
    "\n",
    "    for i in range(10, 201, 10):\n",
    "        rf = RandomForestClassifier(n_estimators=i, max_depth=depth, random_state=1)\n",
    "        rf.fit(X_train, y_train)\n",
    "        y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "        auc = roc_auc_score(y_val, y_pred)\n",
    "        print('%s -> %.3f' % (i, auc))\n",
    "        aucs.append(auc)\n",
    "    \n",
    "    all_aucs[depth] = aucs\n",
    "    print()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mWx4QEMChQ4cQEaZPn87Ro0cJDg4mODiYevXquW44O1fR0dF8//33fPfdd/z5559s3LiRNm3asHPnTnx8fGjcuLEnToUxhc5bxfo6ATtVNcoZxAzgFiDrl7wCFcXRe1cBSADSVDUWiAVQ1ZMish2ok2NbU8xFRETw+eefM2XKFA4fPszp06cpW7Yss2bN4r///S8AAQEBVKhQgcDAQEd9ehEuvfRSGjRoQIUKFVyv2rVruzqBP//8c5YtW+Y6TpkyZbj++uv58ccfAVxPEwsODqZu3brZSl7v3LmT+++/nz/++AOAK664grfeeosaNWoA0LRp00I5N8YUBZ68grgD6KuqDzqn7wM6q+pjWdapCMwDmgMVgbtV9acc+2kI/A60UtUTbo4zHBgOUL9+/fZ79+71yPsxBWfz5s08+eST/Pbbb/j5+dG/f3/uv/9+brrpJvz8/Dh58iSqSrly5fDzO//fMKrKgQMH2LlzJ5GRkURGRhIYGOh6jnD9+vWJjo4GoGzZsjRp0oSnn36aYcOGkZiYSK9evbj55pu58847CQ4OLtD3bkxR460rCHdj+nJmoz7ARuA6oAmwRERWZCYCEakAzAJGuUsOAKo6AZgAjiamggndFLTt27eTkpJCmzZtqFSpEvv372fs2LEMHjyYmjVrZlu3YsWKF3UsEaFOnTrUqVOHbt265Vq+atWqbMkjMjKS0NBQhg0bRvny5V1XD8aUdp5MEDFAvSzTdYEDOdYZAvzH+di7nSKyG8fVxBoR8ceRHKaq6mwPxmk8JDk5mVmzZjFhwgRWrFjBTTfdxI8//kj9+vVdD7Lxhnr16lGvXj169OjhleMbU1x4chTTWiBYRBqJSBlgII7mpKz2AT0BRKQGEAJEOfskJgHbVfV9D8ZoPOTtt9+mdu3a3HfffcTGxvL2228zadIk13K7acyYos9jVxCqmiYijwGLAF9gsqpuE5GHncvHA28CU0RkC44mqRdU9bCIXAPcB2wRkY3OXb6sqgs8Fa+5OElJSXz//ffcddddBAQE4O/vT9++fRk+fDjdunW7oGcfG2O8y55JbS7Ktm3b+Oyzz/jqq684fvw4s2bNssdbGlOM2DOpTYFLSEjglltuYeXKlZQpU4bbb7+dESNGcO2113o7NHMB0tLSLmjEmKelpqZy/Phxjh49yrFjx3L9m/m3j48PXbp0oVu3btSrV+/sOy5hMoeAF7Si94kwRVZ4eDhbt27l9ttvJygoiMqVK/POO+8wePBgqlat6u3wzHlSVZYuXcpbb73F0qVLqVq1Kg0bNqRBgwY0aNDA9Xfmv4GBgRd9vGPHjhEbG0tcXBxxcXGuvw8dOuT2y//UqVP57tPf35+goCBOnz7Np59+CkDjxo3p1q0b3bt3p1u3bjRo0OCi4i6KVJWdO3eycOFCFi5cyL59+9i8eXOBJwlrYioBjh07xtixY6lfvz7t27enTZs2BVYoLiUlhTlz5vDZZ5+xbNkyKleuTGxsrNvHXpriISMjgx9//JExY8awZs0aatWqxb333svx48fZu3cve/fuZc+ePZw+fTrbdoGBgXkmkMqVKxMfH+/2yz/r3ykpKbniKVu2LDVq1CAoKIigoCAqVaqU619384KCgrjkkksQEdLT09myZQu//fYby5cvZ/ny5Rw9ehSAhg0buhJG9+7dadiwYWGc5gJ38uRJli1bxsKFC1m0aBFRUVEABAcH06dPH959913Kli173vvNr4nJEkQJ8OCDD2YbIeTr60tMTAw1a9Zkw4YNJCUl0aZNGypUqHBe+50zZw4PP/wwhw4domHDhgwfPpwhQ4bkum/BFA9paWl89913jB07lq1bt9K4cWNeeOEFHnjggVxfLKpKfHy8K1lkTRyZ/548eTLf41WrVo2aNWtSs2ZNatWq5fbvmjVrEhgYWOC/fDMyMti6dWu2hHHkyBEAV9XdzCuMxo0bF8lRdarK5s2bXVcJq1atIjU1lfLly3PdddfRt29f+vTpQ5MmTS7qOJYgSrDk5GS6dOlClSpV+L//+z/27dvH1q1beeWVVxAR7rnnHqZNm4aI0Lx5c9q1a0fnzp15/PHHc+0rNTWV+fPnExwczOWXX86GDRt44403GDFiBL1797aRSMXUmTNn+PLLL3n77beJioqiZcuWvPTSS9x9990X3O+Q2VyUmTgSEhKoXr2660u/evXq+Pv7F/A7uXAZGRmEhYWxfPlyV9KIj48HoG7dunTu3JlatWpRvXp1qlWrRvXq1bP9XalSpUJJIkeOHGHJkiWuq4S4uDgAWrduTd++fenbty9dunS5oCuFvFiCKMGOHz9Onz59+OuvvwgMDOStt95ixIgRruJ0sbGxhIaGsn79etatW8e6deuoUqUKmzdvBmDo0KEkJydTs2ZNvv32W2JjY3n88cf5+OOPvfm2TAFITExkwoQJvPvuuxw4cICOHTvyyiuvcPPNN5f6ZK+qbN++3ZUwNm7cSHx8vKtZKid/f3+qVavmNnlUr17d1T+jqmRkZKCqbl95LYuOjmbRokWsWbMGVaVy5cpcf/319O3bl969e1O7dm2PnQtLECXUtGnTeOedd9i2bRsffPABs2fPZunSpXTo0IFPP/2UDh3c/jfn1KlTruamIUOG8Ouvv7J//35uuOEGRowYwQ033FAkR7SUNKpKaGgoEydO5Pfff6dBgwY0b96ckJAQ17+1atU671+uR48e5ZNPPuGjjz7iyJEj9OjRg5dffpmePXsWyaaUoiQlJYXDhw9z6NAh4uPjOXTokOuVc/rQoUMkJiYWyHF9fHzo1KmT6yqhQ4cO512B+EJZgiiBVq9ezdVXX42Pjw9z586lX79+qCozZszg6aef5uDBgzzyyCOMGTOGSpUqnXV/qampRapJoCRLSEhg6tSpTJw4kc2bN3PJJZfQo0cPYmNjCQ8PJykpybVuxYoVCQkJyZY0mjdvTnBwMAEBAdn2e/DgQT744AP+97//cfLkSfr168fLL7/MVVddVdhvsdRISkoiPj6e48ePIyL4+PggInm+8loeGBjIpZde6pX3YAmihDl48CCNGzcmKSmJGTNmcPfdd2dbfvz4cV599VXGjRtH1apVee+997jnnnvs16MXZWRksHz5ciZOnMisWbM4c+YM7du356GHHmLgwIGuJoqMjAz2799PeHg4O3bsIDw83PV3ZgVacJQqadiwoSt5nD59mi+//JKUlBTuuusuXnzxRdq0aeOtt2uKkfwSRJ5tZcXx1b59ey3p4uLitFatWgros88+m++669at006dOimg3bp1023bthVSlCbTgQMHdOzYsdqkSRMFNDAwUEeOHKnr168/732dOnVKN2zYoNOnT9fXX39dBw0apFdccYWWK1dO/f39ddiwYRoREeGBd2FKMiBU8/hO9fqXekG+SnqCOHDggDZt2lQBveKKKzQjI+Os26Snp+tnn32mQUFB6ufnpy+++KKeOnWqEKItvVJTU3X+/Pl6yy23qK+vrytBf/3115qUlFTgx0tPT9fk5OQC368pHSxBlADR0dEaHBys5cqV027dup33L8WDBw/q4MGDFdD69evrDz/84KFIS69du3bpK6+8orVr11ZAa9SooS+88IKGh4d7OzRj8mQJopjbu3evNm7cWCtWrKirVq26qH39/vvv2qpVKwX05ptv1t27dxdMkKXUoUOHdOLEidqzZ08F1MfHR2+88UadPXu2pqSkeDs8Y84qvwRRugdDFwO7d++mW7duHDlyhC5duriejXyhunbtyvr163nnnXdYunQpLVq04K233nJbAsG4Fx0dzX//+1969OhBzZo1efDBB4mKiuKNN95gz549/PTTTwwYMMBGhZniL6/MURxfJe0KIjIyUuvVq6dBQUE6cOBAFRH9448/Cmz/+/bt09tuu00BDQkJ0YkTJ2piYmKB7b+wnD59Wvft26dpaWkeO0Z4eLiOHTtWO3bsqDgenastW7bUV199VTds2HBO/UHGFEXkcwVhw1yLqIiICHr06MGZM2f48MMPuf/++xk5ciT//e9/C/xYP//8M88//zxbt26lUqVKDBkyhEceeYTg4OACP9aFUlUOHDjgGvaZ9bV3714yMjIoW7YsISEhtGjRIturadOm5/1rXlXZuHEjc+bMYfbs2Wzbtg2Ajh07cttttzFgwABCQkI88VaNKVR2H0Qxs337dq677jrS09NZuHAhQ4YM4ciRI4SFhXnsZhpVZeXKlYwbN45Zs2aRlpZG7969GTlyJDfddFOh3dWZmJhIREREriQQERGRrfRzuXLlaNasmes+gFq1ahEVFUVYWBhhYWHs3r3bta6fnx/NmjVzJYyWLVvSokULgoODs9W0ycjI4I8//mD27NnMnj2bPXv24OPjw7XXXsuAAQO49dZbqV+/fqGcB2MKiyWIYmTr1q2ukghLly5lyZIljBo1ijlz5nDrrbcWSgyxsbFMnDiRzz77jP3791O/fn0efvhhhg0bRvXq1QvsOEeOHOHPP/9k9erVrFmzhh07dhATE+NaLiI0aNDAlQRCQkJcSaFOnTr51hNKTExkx44droSR+dq1axeZn3lfX1+aNm1KixYtCAwMZOHChcTFxVGmTBl69erFbbfdRv/+/alWrVqBvWdjihpLEMXEpk2b6NWrF/7+/ixdupTmzZtz4sQJvv32Wx566KFCjyctLY158+Yxbtw4li5dSpkyZbjzzjsZOXIkV1555XndmZ2RkUF4eDirV692vXbs2AE4fuG3bt2ali1bZksGTZs2LbDnWmRKTk4mIiLClTC2bdtGWFgYhw4dolevXgwYMIAbb7zxoh+OY0xxYQmiGFi/fj3XX3895cqVY+nSpTRt2pTU1NQi82Ce7du38+mnn/Lll19y4sQJ2rZty8iRIxk0aBDly5fPtf6pU6dYs2aNKxn88ccfHDt2DIAqVarQpUsX16tDhw6UK1eukN+RMQYsQRR5+/bto02bNlx66aUsW7aMxo0b8/XXXzN27Fh++eUXj5b6PV+nTp1i6tSpjBs3ji1bthAYGMiQIUMYOHAgu3btciWETZs2kZGRAUDLli2zJYTg4GCrC2VMEWEJooj75z//ydixY9mxYwfBwcEcPnyY5s2b06xZM1auXFkka/erKqtWrXJ1aqempgJQoUIFOnfu7EoGnTt3JigoyMvRGmPykl+CsKL/Xpaens6UKVPo06ePa1jpM888w/Hjx5kwYUKRTA7g6EC+5ppruOaaa4iLi3PddNeqVSt7loQxJYT9n+xlixcvZv/+/Xz00UcA/PLLL3z11Ve88sortGrVysvRnZuaNWvyj3/8w9thGGMKWNH8eVqKTJo0iapVq3LzzTcD8NlnnxEcHMw///lPL0dmjCnt7ArCi+Lj45k3bx6PPfaYa7TS9OnT2b9/f66nhRljTGGzKwgvmjp1KqmpqQwdOpSoqCgSEhLw8/OjQYMG3g7NGGPsCsJbVJVJkybRsWNHmjdvztVXX01SUhKbNm0qsh3TxpjSxb6JvCQ0NJStW7dy1113ccMNN7BmzRpeeuklSw7GmCLDriC8ZNKkSZQtW5YPP/yQw4cPM2nSJBsJZIwpUixBeEFSUhLTp0+nWrVq+Pn5sXr1atq1a+ftsIwxJhtLEIUsOTmZKVOmcOLECb766iuuueYaqlSp4u2wjDEmF482eItIXxEJF5GdIvKim+WBIjJfRDaJyDYRGZJl2WQROSQiWz0ZY2GKioqiS5cuvPzyyzRp0oT+/ftbcjDGFFkeSxAi4guMA24AWgCDRKRFjtVGAmGq2gboDrwnIpnlS6cAfT0VX2FbsGAB7du3Z9euXRw/fpwhQ4ZYwTpjTJHmySuITsBOVY1S1RRgBnBLjnUUqCiOb8oKQAKQBqCqvzuni7X09HRGjx7NTTfdRIMGDbjvvvvw8fHhgQce8HZoxhiTL08miDpAdJbpGOe8rD4BLgMOAFuAJ1U143wOIiLDRSRURELj4+MvJl6PSExMZOrUqTzwwAOsWLGCuXPn0qdPH+rWrevt0IwxJl+eTBDu2k9y1hbvA2wEagNtgU9E5LweuqyqE1S1g6p2KEqPhtyyZQunT5/m0ksv5a+//uKLL75g5cqV7N+/n6FDh3o7PGOMOStPJogYoF6W6bo4rhSyGgLMVoedwG6guQdjKhRffPEFnTp1YvTo0YDjCWoiwuTJk6latSr9+/f3coTGGHN2nkwQa4FgEWnk7HgeCMzLsc4+oCeAiNQAQoAoD8bkUWfOnGHEiBEMHTqULl268Oyzz7qWHT58mLlz53LvvfcWmceIGmNMfjyWIFQ1DXgMWARsB75T1W0i8rCIPOxc7U2gi4hsAX4FXlDVwwAiMh34AwgRkRgRGeapWAtCdHQ0Xbt2ZcKECbz44ossWrSIrE1e33zzDampqQwbVqTfhjHGuHj0RjlVXQAsyDFvfJa/DwC989h2kCdjK2iJiYkcOHCAOXPmcOutt2ZbpqpMnjyZjh07FpuHABljjN1JXUCaN2/Orl27KFu2bK5loaGhbNmyhfHjx7vZ0hhjiiYrHVoAfv31VzZt2uQ2OQBMnjyZgIAABg4cWMiRGZO/OXPmMHjwYNf04sWL+fXXX70XkClSLEEUgMcff5xXXnnF7bKkpCSmTZvGHXfcQWBgYCFHZkxumzdvJjU1FYADBw6wfv16jh49CsC///1vXn75Zde6s2bNYt26dV6J03ifJYiLdOTIEbZv387VV1/tdvns2bM5ceKEdU6bIuG3336jTZs2zJ8/H4ARI0awadMmgoKCAEdJmG+++QZw9J09+uijfPzxx67tv/zySyIjIws/cENycjLJycmu6YiICA4cyHnnQMGyBHGRVq9eDZBngpg8eTKNGzfm2muvLcywjAEgNTWVp59+mkmTJgFwzTXX8Mknn9C9e3cA/Pz8stUEq1ChAsHBwQCICNu2beONN94AHD+GBg8ezMyZM137njhxInFxca7t09PTychwFEM4fvw4a9euJTExEXAUq/z0009dVyt//vknw4YNI7MCwm+//cagQYM4dOgQACtXruTJJ5/k+PHjAKxZs4Z///vfJCUlAbBp0yYmTJhASkoKALt27eKXX34hPT0dgGPHjhEXF4dqzvtzz82JEyeIiYlxTS9evJhvv/3WNT169Gieeuop1/S9996b7Zku9913Hw899FC26VGjRrmmH3jggWxXa7fddhsvvfSSa7pdu3aMHDnSNd2oUaNsx7vyyiv5z3/+c0Hv7VxZgrhIq1atwt/fn44dO+ZatmvXLpYtW8bQoUPtSXGm0KSkpLBx40YA/P39CQ0Ndf3q9/PzY+TIkVSuXPmc9lW1alXXM9KrVKnCvn37ePDBBwHH4IuHHnqIVatWAbBkyRL8/Pz4888/AVixYgWdOnUiLCwMgI0bN/Loo4+yb98+AGJjY1m4cKErARw+fJh169a5vuAjIiL46quvOHPmDOD4f+3VV191JYSFCxcyYsQI1/rTpk3j+uuvdyWE9957j9q1a7vey2uvvUadOn9X+/nwww+56aabXNNvvPEG119/vWt6+PDhXHfdda7pzz//nH/961+u6WPHjrmSHcBll13GZZdd5pquX79+tpI6VapUcV2pAZQpUwZ/f3/XdPXq1bMt79evH1deeWW2+AcMGOCanjx5MkOGuApge4aqlphX+/bttbB17dpVO3fu7HbZP//5T/Xx8dHo6OhCjsoUJ6dOndKFCxe6PicpKSm6Z88eTUpKuqD9DR06VCtXrqzJycmqqpqWllZgsWaVkZGhYWFhevLkSVVVjYqK0tGjR+vu3btVVTUuLk7nz5+vCQkJqqqamJiosbGxmpqaesHHS0lJ0YyMDFV1nLeYmBjXdExMjP7++++u9desWaMTJkxwTc+ZM0efeeYZ1/THH3+st9xyi2v6k08+0SFDhrimly5dqt99951rOj4+Xo8dO3ZBsRdlQKjm8Z3q9S/1gnx5I0GcOHFCIyIics1PS0vTunXr6g033FDoMZniZcuWLerj46OTJ09WVdWIiAgF9Ouvv1ZV1bCwMK1YsaL+8MMPqqoaHh6uV199ta5cuVJVVXft2qW9e/fW2NhYVVVdv369/vzzz5qenu6Fd2OKm/wShN0HcZEqVqxIxYoVc81fsmQJMTExfPDBB16IyhR1CxcuZNu2bTzzzDO0atWKP//809WUU61aNSZNmsRVV10FwKWXXsqwYcNo1KgRABkZGZQtWxY/P8f/vocOHWLPnj1ERkZSs2ZNrrjiCu+8KVPiiF5gB05R1KFDBw0NDS20433//fds2bKF0aNH5+pjuOuuu1i6dCn79+/P8/4IU3qNGDGCFStWsHHjRqvNZbxKRNapagd3y6zn9CJMnTqVadOm5UoOhw8f5ocffuC+++6z5GAAiIuL4/777yc8PByAd955x5KDKfIsQVwgVWXVqlVuh7dOnTqV1NRUe+6DcRERFi1a5Lrp7NJLL7XkYIo864O4QJGRkcTHx+dKEKrKpEmT6NixI5dffrmXojNFwYQJE1i9ejVTpkyhRo0a7Nmzh0suucTbYRlzzuwK4gJljv3OmSDWrVvHli1b7OqhlMrap5eQkEBsbKzr7ldLDqa4sQRxgRISEqhfvz7Nm2d/AN6kSZMICAhg0KBiVa3cFIA9e/bQuXNnV7G7559/nkWLFlliMMWWJYgL9Mwzz7Bnz55sHdRWmK/0iYqKYs2aNQDUrFmTsmXLcvr0aQC7e94Ue9YHcRGy1rABR+lkK8xXsq1cuZKIiAhXE+KIESM4cuQI69evJyAggBUrVng5QmMKjv3EuQALFiygXbt2REVlf3z2pEmTrDBfCfPTTz8xfPhw1/T06dN59tlnXX0NY8eO5csvv/RWeMZ4lCWIC/D777+zdetWatWq5ZoXFRXFsmXLGDJkiDUtFCOqytGjR13PR5g3bx5t2rTh5MmTAOzcuZMlS5a4Csq9/vrrREdHu64eO3ToYKPVTIll32QXYOXKlbRv3z5b5+MXX3yBiGR7Opfxnsxf+EePHuWHH35wlaTesmULt9xyC9u2bQNg/vz5VK5cmU2bNgGO0im1a9cmISEBcDwMavfu3a4+pWrVqlG+fPnCfjvGeIUliPN05swZQkNDsw1vTU9PZ8qUKfTp0ydbeV/jeceOHeOjjz5yfeGHhYVRqVIl5syZAziu7AYMGOAqQZ2RkcHu3bs5ceIEAG3btuX99993XQ326NGDn3/+2VUXya4GTWlmn/7ztG7dOs6cOZMtQaxdu5aYmBgeeOABL0ZWOqSkpPDRRx+5hpKmp6czatQo13SNGjW4//77XV/wLVq0YP369fTs2ROANm3asHnzZlchvPr16/PUU09le06AMcbBRjGdp7Jly3LnnXdmSxDbt28HHO3RpuCNHz+e8uXLc9999+Hv78+YMWO455576NmzJ1WqVCE2NpaaNWsCjoeyZH1E5iWXXGLVTY25QHkmCBHpA1RU1e9zzL8HOKSqSzwdXFHUvn17vvvuu2zzIiMj8fPzo2HDht4JqoSZNm0a0dHRvPDCC4CjtlXVqlW57777EBHCw8OzPXkrMzkYYwpWfk1M/wKWu5n/K/CGZ8Ip2lSV/fv355ofERFB48aNXfX5zflZsGABTzzxhGt66dKl2Z79+/PPP7v6FIBsycEY4zn5JYhyqhqfc6aqxgGlchhHZGQkdevW5euvv842PyIigmbNmnkpquJn06ZNPPHEE65nDYeFhTF37lxOnToFwLhx41i/fr1r/QoVKnglTmNKu/wSRICI5PpJLCL+QKksLpNZoC9rX0NGRgY7d+60BJGP2NhY3nzzTdfD6vft28fkyZNdz0Z48skn2bNnjysR2DM0jCka8ksQs4HPRcR1teD8e7xzWamzcuVKKleuTEhIiGve/v37SU5OJjg42IuRFS1JSUlMnDjRdRVw8uRJRo8ezR9//AFA3759SUhIoHXr1gD4+/vnKltijPG+/BLEP4GDwF4RWSci64E9QLxzWamzatUqunTpkm1sfEREBECpvoLIyMjgp59+YtmyZYCjRtXjjz/OrFmzAAgODubQoUPcfffdgCMh2MNyjCn68uxVVdU04EUR+RfQ1Dl7p6omF0pkRczhw4cJDw/Pdad0aU0QiYmJ7Nq1i9atWyMiPPXUU4SEhNCjRw8uueQSwsPDqVevHuBIGFWrVvVyxMaY85XfMNfbcsxSoJKIbFTVk54Nq+gJCAjg66+/plOnTtnmR0ZGUq5cOWrXru2lyArP6dOnCQgIAODee+9lw4YN7N69GxFhwYIF2e4ir1+/vrfCNMYUkPzGZd7sZl5loLWIDFPVpWfbuYj0BT4CfIGJqvqfHMsDgW+A+s5Y3lXVL85l28JWoUIF7r333lzzIyIiaNq0aYkvyTBhwgSee+45oqOjufTSS3nhhRc4ffo0qoqI0LRp07PvxBhTrOT5raaqQ9y8bgG6A2PPtmMR8QXGATcALYBBItIix2ojgTBVbePc73siUuYcty1Us2fPdjUnZVWQQ1yXLFnCmDFjXJVFsz6+srCFhYXRp08fwsLCAGjXrh3Dhg1zPQznyiuvpHv37iU+MRpTmp33/92quhfwP4dVO+Hos4hS1RRgBnBLzt0BFcUxhKUCkACkneO2hebMmTP84x//4PPPP882PzU1ld27d19wgjh27BiPPfaYa/jnsmXLeOedd1w33D377LO0aNHClShWrVrF0qVnvXC7IMnJyYwbN871wJugoCB2797NgQMHAMfQ3vfff5/q1at75PjGmKLnvBOEiDQHzpzDqnWA6CzTMc55WX0CXAYcALYAT6pqxjlumxnPcBEJFZHQ+Phc9/UVCHcF+sDxDOK0tLQLHuJ67NgxvvnmG9fon7feeou4uDjXkM+OHTty6623uqbfeecdHn/8cdf2zz33HKNGjXJNz5w5k3nz5rmm33zzTcaPH++aHjRoEK+//rprunXr1owcORIAPz8/XnvtNebOnQtArVq1iIiIoFevXhf03owxxV9+ndTzcfzCz6oyUAvI3RjvZhdu5uXcXx9gI3Ad0ARYIiIrznFbx0zVCcAEgA4dOnikTWblypUAdOnSJdv8CxnBFB8fz5w5cxg+fDgNGzZk9+7d2UpHZHYCAwwcOJCBAwe6pidMmEDWJJiSkuJqjgJ49913CQoKon///oCjZEWjRo14+OGHAShTpgz+/n9f/N12222u5Obv709YWBg1atQ45/dijCnZ8uukfjfHtOJoAqqMI0H8cZZ9xwD1skzXxXGlkNUQ4D/qaEPZKSK7gebnuG2hWbVqFcHBwbmaVy4kQXz66aeMGTOG3r1707Bhw/OqK1S9evVsMXz00UfZlv/888/Z7i/IvDLJlPPRmFmvJgBLDsaYbPLrpF6e+QKOA/2AH3EU8dt+DvteCwSLSCMRKQMMBOblWGcf0BNARGoAIUDUOW5bKFSVP//8M1fzEjiGuAYFBVGlSpV89xETE+Pq7H3++efZuHGjRyq/Vq5c2eoWGWMKTH5NTM1wfDEPAo4A3wKiqj3OZceqmiYijwGLcAxVnayq20TkYefy8cCbwBQR2YKjWekFVT3sPH6ubS/wPV4UEWHHjh0kJibmWhYREUFwcHC+ZSIyMjK4/vrrCQoKYtWqVQQEBHDZZZd5MmRjjCkQktdQShHJAFYAw1R1p3NelKo2LsT4zkuHDh00NDS00I5Xv359unXrlqu6KzgK0tWrVw8RYcWKFdSpU4fGjYvsqTPGlFIisk5V3T7tLL9RTLcDccAyEflcRHrivvO4RPv444+zPaEsU1JSEtHR0W77H7Zs2ULz5s2ZPHkyAF27drXkYIwpdvLrg5ijqnfj6DT+DXgKqCEin4pI70KKz+v+97//8csvv+Sav2vXLiB7B3XmTWQtW7bk2WefpW/fvoUTpDHGeMBZ74NQ1URVnaqq/XCMJtoIvOjpwIqC+Ph4wsPD3XZQZ45gyhwmOnHiRFq0aMHx48fx8fHhjTfeoE4dt7duGGNMsXBeN8qpaoKqfqaq13kqoKJk9erVAOeUINq2bcvVV19Nenp64QVojDEeZA9RzseqVasoU6ZMtifIZYqMjKRWrVpUrFgRcJSicNdZbYwxxZVVWstHUlIS11xzTba7mzNlDnHdt28fTz/9tKtmkTHGlBSWIPLxySefuO2ghr+ruK5evZr//e9/2UpeGGNMSWAJ4izc3QR37Ngx4uPjadasGQMHDuTgwYM0aNDAC9EZY4znWILIw4cffsiVV17pGrqaVWRkJPB3B3VgYGChxmaMMYXBEkQeli1bxtGjR/PsfwBH4b1HH320sEMzxphCYQnCDVVl1apVboe3giNBiAiXX365PXvZGFNi2TBXN8LDwzly5Ei+CaJhw4a8+27OiujGGFNy2BWEG6tWrQLc3yAHf98DYYwxJZklCDfq1q3LP/7xD0JCQnItU1W2b9/O6tWr+emnn7wQnTHGFA5rYnKjT58+9OnTx+2ygwcPkpSUxB133EHPnj0LOTJjjCk8dgWRQ1JSEgcPHsxzeeYQ1wcffNDtCCdjjCkpLEHksGTJEmrWrMmaNWvcLv/2228BaNq0aWGGZYwxhc4SRA6ZBfpat27tdvn8+fMREY88U9oYY4oS64PIYeXKlXTo0CHP5qMrrriCsmXL4uvrW8iRGWNM4bIriCxOnz7NunXr8hzeCrBz505atGhRiFEZY4x3WILIIjQ0lJSUFLcJ4ujRo3Ts2NFV5tsYY0o6SxBZhISEMGnSJLp27ZprWWxsLCkpKaSmpmZ7DrUxxpRUliCyqFatGkOHDqVy5cq5lrVo0cJVWsMShDGmNLAE4aSqTJkyhejo6FzLjhw5wpkzZ1xVXC1BGGNKA0sQTuHh4QwZMoTFixfnWvbiiy8SEhLCjh07qFChAjVr1vRChMYYU7hsmKtTZoG+a665JteyQYMG0a5dO+bNm0dwcLDbp8wZY0xJY1cQTitXrqRq1apum4+uu+46HnnkESIjI615yRhTaliCcFq1ahVdunTJdXXwxRdfcPDgQVJSUti9e7cNcTXGlBqWIHB0QkdGRua6/2Hnzp0MHTqUGTNmEBUVRUZGhl1BGGNKDeuDAKpUqcLBgwdzlc9o2rQp27dvp2bNmvz++++AjWAyxpQeliCcqlev7nZ+8+bNgb/LfFsTkzGmtPBoE5OI9BWRcBHZKSIvuln+nIhsdL62iki6iFR2LnvSOW+biIzyZJzufP/994wcOZJTp04BjudQV6lSxe1NdMYYUxJ5LEGIiC8wDrgBaAEMEpFsVe5U9R1VbauqbYGXgOWqmiAirYCHgE5AG6CfiBTqT/edO3eyYsUKypcvDzgShDUvGWNKE09eQXQCdqpqlKqmADOAW/JZfxAw3fn3ZcCfqpqkqmnAcmCAB2PN5cUXX2TDhg2uUU02xNUYU9p4MkHUAbLWrYhxzstFRMoBfYFZzllbgWtFpIpz2Y1APQ/Gmk1iYiKAq9P61KlT7N+/3xKEMaZU8WSCcHe7seax7s3AKlVNAFDV7cDbwBJgIbAJSHN7EJHhIhIqIqHx8fEXHfSZM2do0qQJY8eOdc3buXMnYB3UxpjSxZMJIobsv/rrAgfyWHcgfzcvAaCqk1S1napeCyQAke42VNUJqtpBVTtUq1btooM+c+YMDz74IFdddZVrnhXpM8aURp4c5roWCBaRRsB+HEngHzlXEpFAoBtwb4751VX1kIjUB24Drsq5rSdceuml/Pvf/842L3OIa9OmTQsjBGOMKRI8liBUNU1EHgMWAb7AZFXdJiIPO5ePd646AFisqok5djFLRKoAqcBIVT3qqVgz7d+/n3379nHllVdmK7kRERFBnTp1XCOajDGmNPDojXKqugBYkGPe+BzTU4ApbrbN/Vg3Dxs/fjxvvfUW0dHR1K5d2zXfhrgaY0ojq8WUxfPPP8+PP/6YLTmAJQhjTOlkCSKLihUrcsMNN2Sbd+TIERISEixBGGNKHUsQTqNHj+bHH3/MNd9qMBljSitLEMDp06eZOnUqf/zxR65lNsTVGFNaWTVXICAggIiICE6fPp1rWWRkJL6+vjRq1MgLkRljjPfYFYSTj48P5cqVyzU/IiKCRo0aUaZMGS9EZYwx3mMJ4iwiIiKs/8EYUypZE1M+VJXIyEi6devm7VCMKVVSU1OJiYlx2+xrLkxAQAB169bF39//nLexBJGP2NhYEhMTrYPamEIWExNDxYoVadiwYbaqBubCqCpHjhwhJibmvPpTrYkpH5kjmKyJyZjCdfr0aapUqWLJoYCICFWqVDnvKzJLEPmwIa7GeI8lh4J1IefTEkQ+IiMjKVu2LPXqFdqziowxpsiwBJGPzBFMPj52mowp7V5//XXefffd895u48aNLFjwd83SC91P5rZ16tShbdu2tG3bNtt+PcG++fJhQ1yNMRcrZ4K4WE899RQbN25k48aN3HjjjQW2X3csQeQhLS2NXbt2Wf+DMUVA9+7dmTJlCuAYAtu9e3e++eYbAJKSkujevTvffvstAMePH6d79+7Mnj0bgMOHD9O9e3fmz58PQFxc3Dkfd8yYMYSEhNCrVy/Cw8MB2LVrF3379qV9+/Z07dqVHTt2ADB48GAefvhhunbtSrNmzfjxxx9JSUnhtdde49tvv6Vt27auGMPCwujevTuNGzfm448/vvgT5CE2zDUP+/btIzU11RKEMaXUunXrmDFjBhs2bCAtLY127drRvn17hg8fzvjx4wkODuavv/7i0UcfZenSpQDs2bOH5cuXs2vXLnr06MHOnTt54403CA0N5ZNPPgEczUQ7duxg2bJlnDx5kpCQEB555BH8/f3p2rUrJ0+ezBXLu+++S69evQD45JNP+Oqrr+jQoQPvvfceQUFBHjsHliDyYENcjSk6fvvtN9ff/v7+2abLlSuXbTowMDDbdNWqVbNN16xZ85yOuWLFCgYMGOAqwdO/f39Onz7N6tWrufPOO13rnTlzxvX3XXfdhY+PD8HBwTRu3Nh1dZHTTTfdRNmyZSlbtizVq1fn4MGD1K1blxUrVuQb0yOPPMKrr76KiPDqq6/yzDPPMHny5HN6PxfCEkQebIirMSbn0NCMjAwqVarExo0bz2n9vIaWli1b1vW3r68vaWlpAGe9gqhRo4Zr3kMPPUS/fv3O6X1cKOuDyENERASXXnop1atX93YoxhgvuPbaa5kzZw7JycmcPHmS+fPnU65cORo1asTMmTMBxx3KmzZtcm0zc+ZMMjIy2LVrF1FRUYSEhFCxYkW3X/rurFixwtUBnfWV2bwUGxvrWnfOnDm0atWqAN9xbpYg8hAZGUmzZs3sZh1jSql27dpx991307ZtW26//Xa6du0KwNSpU5k0aRJt2rShZcuWzJ0717VNSEgI3bp144YbbmD8+PEEBATQo0cPwsLCsnVSX6jnn3+eyy+/nNatW7Ns2TI++OCDi9rf2YiqevQAhalDhw4aGhpaIPtq1KgRV111FdOmTSuQ/Rljzt327du57LLLvB3GeRk8eDD9+vXjjjvu8HYoeXJ3XkVknap2cLe+XUG4cfr0afbu3Wv9D8aYUs06qd2IiopCVS1BGGPOWeZ9GiWJXUG4YSOYjDHGEoRbdg+EMcZYgnArIiKC6tWrExgY6O1QjDHGayxBuJE5xNUYY0ozSxBuWBVXY0xORaHc98yZM2nZsiU+Pj7kHNI/duxYmjZtSkhICIsWLbqg/edkCSKHEydOEBcXZ1cQxpgCUZDlvlu1asXs2bO59tprs80PCwtjxowZbNu2jYULF/Loo4+Snp5+0cezYa45REZGAjaCyZiipHv37rnm3XXXXTz66KMkJSW5fS7C4MGDGTx4MIcPH85181rW4n35GTNmDF999RX16tWjWrVqtG/fnl27djFy5Eji4+MpV64cn3/+Oc2bN2fw4MEEBASwbds2Dh48yPvvv0/v3r157bXXSE5OZuXKlbz00kvA3+W+9+3bx6hRo3jiiSfOKZ68bh6cO3cuAwcOpGzZsjRq1IimTZuyZs0arrrqqnPab14sQeRgCcIYA0W33Lc7+/fv58orr3RN161bl/3791/0ObAEkUPmENcmTZp4ORJjTKb8fvHnLPedU85y3+eqKJb7zou7kkkFUUfOowlCRPoCHwG+wERV/U+O5c8B92SJ5TKgmqomiMhTwIOAAluAIap62pPxgiNB1K9fn0suucTThzLGFHFFrdx3XurWrUt0dLRrOiYmhtq1a+e5/rnyWCe1iPgC44AbgBbAIBFpkXUdVX1HVduqalvgJWC5MznUAZ4AOqhqKxwJZqCnYs3KhrgaY6BolvvOS//+/ZkxYwZnzpxh9+7dREZG0qlTpwt/806eHMXUCdipqlGqmgLMAG7JZ/1BwPQs037AJSLiB5QDDngsUidVJSIiwhKEMaZIlvueM2cOdevW5Y8//uCmm26iT58+ALRs2ZK77rqLFi1a0LdvX8aNG4evr+9FHQs8WO5bRO4A+qrqg87p+4DOqvqYm3XLATFAU1VNcM57EhgDJAOLVfWenNs51xsODAeoX79++717915wzPHx8VSvXp0PPviAUaNGXfB+jDEXx8p9e0ZRKvftrvEtr2x0M7AqS3IIwnG10QioDZQXkXvdbaiqE1S1g6p2qFat2kUFbEX6jDHmb57spI4B6mWZrkvezUQDyd681AvYrarxACIyG+gCfOOBOF1siKsx5kJZue/zsxYIFpFGIlIGRxKYl3MlEQkEugFzs8zeB1wpIuXEMQygJ7Ddg7ECjisIPz8/GjZs6OlDGWNMkeexKwhVTRORx4BFOEYhTVbVbSLysHP5eOeqA3D0MSRm2fYvEfkeWA+kARuACZ6KNVNERASNGzfGz89uDzHGGI9+E6rqAmBBjnnjc0xPAaa42XY0MNqD4eViQ1yNMeZvVqzPKSMjwxKEMcZkYQnCaf/+/SQnJ1uZb2OMW0Wh3Pdzzz1H8+bNad26NQMGDODYsWOuZVbu24NsiKsxxhMKstz39ddfz9atW9m8eTPNmjVj7NixgJX79jgb4mpM0TRq1Kg8ax9dqLZt2/Lhhx+edb2iVu67d+/err+vvPJKvv/+e8DKfXtcREQE5cqVK5ACV8aY4q+ol/uePHkyd999N2Dlvj0uIiKCpk2b4uNjrW7GFCXn8kvfE4pyue8xY8bg5+fHPfc4KhAVy3LfxUlERARt2rTxdhjGmCKkKJb7/vLLL/nxxx/59ddfXfsvduW+i5PU1FR2795t/Q/GGJeiWO574cKFvP3228ybN891ZQPFs9x3sbFnzx7S0tJsiKsxxqUolvt+7LHHOHnyJNdffz1t27bl4YcfBophuW9v6NChg4aGhp73dhEREbzyyiu88sortG3btuADM8acFyv37RnnW+7b+iBwDG3NvGQ0xhjjYAnCGGMKgJX7NsaYQlKSmr+Lggs5n5YgjDFFTkBAAEeOHLEkUUBUlSNHjhAQEHBe21kTkzGmyKlbty4xMTHEx8d7O5QSIyAggLp1657XNpYgjDFFjr+/P40aNfJ2GKWeNTEZY4xxyxKEMcYYtyxBGGOMcatE3UktIieBcG/HUUxVBQ57O4hizM7fxbHzd3Eu5vw1UNVq7haUtE7q8LxuGTf5E5FQO3cXzs7fxbHzd3E8df6sickYY4xbliCMMca4VdISxARvB1CM2bm7OHb+Lo6dv4vjkfNXojqpjTHGFJySdgVhjDGmgFiCMMYY41aJSBAi0ldEwkVkp4i86O14igMR2SMiW0Rko4iEOudVFpElIhLp/DfI23EWFSIyWUQOicjWLPPyPF8i8pLz8xguIn28E3XRkcf5e11E9js/gxtF5MYsy+z8OYlIPRFZJiLbRWSbiDzpnO/xz1+xTxAi4guMA24AWgCDRKSFd6MqNnqoatss46dfBH5V1WDgV+e0cZgC9M0xz+35cn7+BgItndv8z/k5Lc2mkPv8AXzg/Ay2VdUFYOfPjTTgGVW9DLgSGOk8Rx7//BX7BAF0AnaqapSqpgAzgFu8HFNxdQvwpfPvL4FbvRdK0aKqvwMJOWbndb5uAWao6hlV3Q3sxPE5LbXyOH95sfOXharGqup6598nge1AHQrh81cSEkQdIDrLdIxznsmfAotFZJ2IDHfOq6GqseD4UALVvRZd8ZDX+bLP5Ll7TEQ2O5ugMptI7PzlQUQaAlcAf1EIn7+SkCDEzTwbu3t2V6tqOxxNcyNF5FpvB1SC2Gfy3HwKNAHaArHAe875dv7cEJEKwCxglKqeyG9VN/Mu6PyVhAQRA9TLMl0XOOClWIoNVT3g/PcQMAfHJehBEakF4Pz3kPciLBbyOl/2mTwHqnpQVdNVNQP4nL+bQez85SAi/jiSw1RVne2c7fHPX0lIEGuBYBFpJCJlcHTOzPNyTEWaiJQXkYqZfwO9ga04ztsDztUeAOZ6J8JiI6/zNQ8YKCJlRaQREAys8UJ8RVrml5vTAByfQbDzl42ICDAJ2K6q72dZ5PHPX7Gv5qqqaSLyGLAI8AUmq+o2L4dV1NUA5jg+d/gB01R1oYisBb4TkWHAPuBOL8ZYpIjIdKA7UFVEYoDRwH9wc75UdZuIfAeE4RiBMlJV070SeBGRx/nrLiJtcTR/7AFGgJ0/N64G7gO2iMhG57yXKYTPn5XaMMYY41ZJaGIyxhjjAZYgjDHGuGUJwhhjjFuWIIwxxrhlCcIYY4xbliBMiSAiKiLvZZl+VkReL6B9TxGROwpiX2c5zp3Oip3LcsxvKCL/8PTxjcnJEoQpKc4At4lIVW8HktV5VtEcBjyqqj1yzG8IuE0QIlLs72UyRZclCFNSpOF4Lu9TORfkvAIQkVPOf7uLyHIR+U5EIkTkPyJyj4iscT4ro0mW3fQSkRXO9fo5t/cVkXdEZK2z4NyILPtdJiLTgC1u4hnk3P9WEXnbOe814BpgvIi8k2OT/wBdnc9MeEpEBovITBGZj6PgYnlnsbu1IrJBRG45S3y1ROR35/62ikjXCzznpoSzXx+mJBkHbBaR/zuPbdoAl+EoRR0FTFTVTuJ4KMvjwCjneg2BbjiKyy0TkabA/cBxVe0oImWBVSKy2Ll+J6CVs9yyi4jUBt4G2gNHcXzB36qqb4jIdcCzqhqaI8YXnfMzE9Ng4CqgtaomiMhbwFJVHSoilYA1IvILcE8e8d0GLFLVMc4rnHLncb5MKWIJwpQYqnpCRL4CngCSz3GztZklk0VkF5D5Bb8FyNrU852zqFykiEQBzXHUsGqd5eokEEfdmxRgTc7k4NQR+E1V453HnApcC/xwjvFmWqKqmc9X6A30F5FnndMBQP184lsLTHYWgPtBVTee57FNKWEJwpQ0HwLrgS+yzEvD2ZzqLHxWJsuyM1n+zsgynUH2/z9y1qRRHGWVH1fVRVkXiEh3IDGP+NyVYr4QWfcvwO2qGp4jDrfxOZddC9wEfC0i76jqVwUUlylBrA/ClCjOX9Xf4ejwzbQHR5MOOJ625X8Bu75TRHyc/RKNgXAcBSIfcf4SR0SaOavj5ucvoJuIVHU27wwClp9lm5NAxXyWLwIedyYEROSKLPNzxSciDYBDqvo5jiqh7c5yfFNK2RWEKYneAx7LMv05MFdE1uB4dm9ev+7zE47ji7wG8LCqnhaRiTj6JtY7v5zjOctjWlU1VkReApbh+OW/QFXPVlZ9M5AmIptwPNv5aI7lb+K4ctrsjGMP0A/IK77uwHMikgqcwtGXYkwuVs3VGGOMW9bEZIwxxi1LEMYYY9yyBGGMMcYtSxDGGGPcsgRhjDHGLUsQxhhj3LIEYYwxxq3/B63NBDMcUDZSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "num_trees = list(range(10, 201, 10))\n",
    "\n",
    "plt.plot(num_trees, all_aucs[5], label='depth=5', color='black', linestyle='dotted')\n",
    "plt.plot(num_trees, all_aucs[10], label='depth=10', color='black', linestyle='dashed')\n",
    "plt.plot(num_trees, all_aucs[20], label='depth=20', color='black', linestyle='solid')\n",
    "    \n",
    "plt.xticks(range(0, 201, 50))\n",
    "plt.legend()\n",
    "\n",
    "plt.title('Number of trees vs AUC')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_random_forest_n_estimators_depth.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuning the `min_samples_leaf` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_samples_leaf: 3\n",
      "10 -> 0.811\n",
      "30 -> 0.823\n",
      "50 -> 0.823\n",
      "70 -> 0.822\n",
      "90 -> 0.823\n",
      "110 -> 0.823\n",
      "130 -> 0.824\n",
      "150 -> 0.825\n",
      "170 -> 0.825\n",
      "190 -> 0.825\n",
      "\n",
      "min_samples_leaf: 5\n",
      "10 -> 0.813\n",
      "30 -> 0.818\n",
      "50 -> 0.821\n",
      "70 -> 0.822\n",
      "90 -> 0.822\n",
      "110 -> 0.825\n",
      "130 -> 0.825\n",
      "150 -> 0.825\n",
      "170 -> 0.825\n",
      "190 -> 0.826\n",
      "\n",
      "min_samples_leaf: 10\n",
      "10 -> 0.810\n",
      "30 -> 0.822\n",
      "50 -> 0.822\n",
      "70 -> 0.819\n",
      "90 -> 0.820\n",
      "110 -> 0.820\n",
      "130 -> 0.821\n",
      "150 -> 0.820\n",
      "170 -> 0.820\n",
      "190 -> 0.820\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_aucs = {}\n",
    "\n",
    "for m in [3, 5, 10]:\n",
    "    print('min_samples_leaf: %s' % m)\n",
    "    aucs = []\n",
    "\n",
    "    for i in range(10, 201, 20):\n",
    "        rf = RandomForestClassifier(n_estimators=i, max_depth=10, min_samples_leaf=m, random_state=1)\n",
    "        rf.fit(X_train, y_train)\n",
    "        y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "        auc = roc_auc_score(y_val, y_pred)\n",
    "        print('%s -> %.3f' % (i, auc))\n",
    "        aucs.append(auc)\n",
    "    \n",
    "    all_aucs[m] = aucs\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hVKlShWPHjhEaGkrr1q0pWrRo3ql5OGX3MmWv1y5y42E1GHO9O3funC5ZskSPHTumqqpTpkxRQMuUKaO9e/fW//znPxoaGqrJyclejvTKkpOT9ciRI65f+HFxcfqPf/xDJ0+erKqqZ8+eVUA/+ugjVVWNjo5WQD///HNVVT1+/LiKiE6cOFFVVcPDw7Vhw4Y6Z84c13LXrl31888/13nz5unnn3+ud9xxh44ZM0ZVVSMjI/Xee+/VW2+9Vdu2bat16tTRsmXLaocOHVTVUetq1qyZNm3aVMeNG6dRUVG59t54AlaDyZjVYMz1JikpiS1btrB8+XKWL1/OunXriI+PZ/LkyQwZMoTIyEgOHDhAq1atKFCggLfDvYy6/ZL//PPPqVixIgMGDEBVKVOmDAMGDGDChAkA3HXXXfTp04fBgweTnJzMwoULady4MXXr1iU5OZl9+/ZRuXJlSpUqxaXvO0/WEi7VAvNSTSSr7E7+TLAEY64H+/fv5/z58zRp0oTjx49TuXJlAJo1a0bXrl254447uPXWWylevLiXI01p27ZtREdH4+/vD0DPnj3x9fVl3rx5ALRq1YqGDRsyc+ZMAObOnUudOnVo2bKlt0K+btid/MZcxzZt2sTXX3/N8uXLOXDgAPfccw8LFy6kUqVKLFiwgHbt2rkuafUWVSUyMpJL02Z89dVXbN++nYkTJwLw+uuvc+DAAXbs2AFA586dU1z1FRAQgK+vr2v5wQcfzMXoTXZYgjEmnxo/fjzPP/88xYoV4x//+AcjR46kW7duru09e/b0SlxBQUGsWrWKF154AYDnn3+e7777jlOnTiEiREREsG/fPtf+H374IQUL/u+raNSoUSnKc08uJn+xBGNMPlWpUiXuu+8+vv76a0qXLu21OBYsWMCYMWP466+/8PPzY+XKlYwaNYrBgwdTpkwZHnjgAerXr09SUhIFCxZk7NixKY5v1KiRlyI3nubJCceMMTksICCA7777DoCHHnqIOXPm5HpyOXPmDJMnT3ZNyVumTBkqVqzImTNnAMdd/rGxsZQpUwZwNHk988wzKWop5vpgCcaYfEBV+fzzz7n11lt5//33XcOu5NaVSgkJCURFRQFw6tQpnnrqKX755RcAbr31Vn7//XfXUDElS5b0+DwlJn/waIIRkTtFZI+IhIrIK2lsLyUiC0Vkm4jsEpHBzvXVRWSliIQ41z+XxrGjRERFJH/cCWZMFl0aZfeFF17gnnvuYf369bnaL5GcnEzDhg0ZPXo0ADVq1GD37t0MHz4812Iw+ZPH6qwiUgCYAHQFwoBNIrJAVYPddhsGBKtqTxGpAOwRkZlAIvCiqgaJiB+wWUSWXzpWRKo7yz3sqfiNyQvOnTtHmzZtOHLkCF988QUjRozIlVrL1KlTWbt2Ld988w0+Pj6MHj2aWrVqubbXr1/f4zGY/M+TjaLtgFBV3Q8gIj8AvQD3BKOAnzj+x5QATgGJqhoBRACo6hkRCQFucDv2c+Al4BcPxm+M1xUvXpxnn32WW265hXbt2nnsPGfOnGHRokU8/PDD+Pj4EB4ezr59+1xDsDz11FMeO7e5dnmyiewG4IjbcphznbvxQEMgHNgBPKeqye47iEhNoCWwwbl8L3BUVbdd6eQi8qSIBIpI4MmTJ7PzOozJVTExMfTp04dVq1YBjst8PZFckpKSXH05ixYton///qxbtw6AV199lTVr1lC4cOEcP6+5fngywaRVj089bEB3YCtQFWgBjBeRkq4CREoA84DnVTVWRIoBY4A3Mjq5qk5W1Taq2ubSDV7G5HWbN2+mdevW/PTTT+zdu9dj5zl69Cg33ngj//3vfwHo1asXa9eupVOnTgBpDsNvzNXy5KcoDKjutlwNR03F3WBgvnNctVDgANAAQEQK4UguM1V1vnP/OkAtYJuIHHSWGSQilT32KozJBarKhAkTuOWWW0hISGDNmjU8+eSTOXqO8ePHM2nSJACqVq1Kz549qVOnDgDFihWjY8eO18T4WSbv8GSC2QTUE5FaIuIL9AUWpNrnMNAFQEQqAfWB/c4+mW+AEFX97NLOqrpDVSuqak1VrYkjibVS1WMefB3GeNyCBQsYPnw4d9xxB1u2bKFDhw7ZLjMuLo7169e7ln/99VeWLFkCOC5vnjhxomv8L2M8wWOd/KqaKCLDgaVAAeBbVd0lIkOd2ycCY4FpIrIDR5Pay6oaKSKdgEeBHSKy1Vnka6q62FPxGuMNFy5coGjRovTs2ZMff/yR3r17Z6t5yn0U4ldffZWJEydy/PhxSpYsybx58yhWrFhOhW5Mhmw0ZWO8QFWZOHEiY8eOZf369dSoUSPbZf7111888sgj/Pbbb9SvX5+9e/dy5MgR/P398+SQ/Cbvy+5oytaTZ0wui42NpV+/fjzzzDM0b948Ra0iPj6euLg41/LWrVs5ePCga3n69Ols2rQJcNwA+eKLL7qawWrVqkXdunU5d+4cADfddBNdunSx5GK8xhKMMdlw+PBhDhw44FpesWIFa9ascS1PmDCBWbNmuZYffvhh6tSpw5w5c3j//fc5e/YsH3/8sWv7DTfcwIsvvuha7ty5M1988YVr+emnn+bHH38EHP0o33zzDT///DMAVapUYdmyZbRq1SqnX6YxWWKjzxmTDf369aNo0aKsWLECcPR7lC9fnttuuw2AKVOmULduXfr37w/A+vXriYuLY+XKldx2220cO3bMdSUXwBtvvMFNN93kWp49ezY33nijazk4ONg1iKSIEBMT4+mXaEyWWR+MMVdh3bp1vPzyyyxatIjSpUvz+++/U7BgQTp37gxAaGgohQsXpnp1xxX6ycnJnD9/nsjISGrWrMnZs2c5f/48FStW9ObLMCZTbEZLY3JR0aJFiYmJITw8nNKlS9OlS5cU2+vWrZtiedeuXTz00EMUKVKEoKAgSpQoQYkSJXIzZGO8xvpgjMnAtGnT+PDDDwHH/PDbt2/PcJIsVeWbb76hXbt2nD59mi+++MLujjfXHfvEmzQlJycTHBxMWFgYACdPnqRz587MmzfPy5HlvtWrV7Ns2TKSkpKAjIdROX/+PAMHDuSJJ56gY8eObN261W5oNNclSzDXscTERGJjYwHHL+5nn32W77//HnBMMNWkSROmTJkCQOnSpXHvr9u5cycjR47k1KlTuR+4h8XHx/P++++7rg6bMGECK1asyPTlvj4+PoSEhPDWW2+xdOlSKlWq5MlwjcmzLMFcR2bMmMHcuXNdy7Vr12bkyJGA44qklStXEhISAkDhwoWZN28ejz76KACFChVizZo19O7dG4C1a9e6pu4FuJYuFjlx4gQffPCB63LgYsWKZap564cffiAmJoYiRYrw119/8eabb9o9KOb6pqrX/KN169Z6Pdi6dasuX77ctdy/f3+97777XMs333yzdu3a1bX8n//8R3/99dcsn+/06dOu5w888IB++OGHWS7L22JiYvTbb791LR86dCjTx547d04HDRqkgL799tueCM8YrwACNRvfvXYVWT5y9uxZjhw5QsOGDQH48ssvCQgIcDVrvffee2zZsoV9+/YB0LJlS9d8HwCLFy+mVKlSruWhQ4dmK56SJR0zK8THx1OkSBEKFSoEOH60nD59mtKlS2er/Nw0adIkXn31VW655Rbq16+f4t6TKwkJCeGhhx4iODiYN954gzFjxng4UmPykexkp/zyuFZqMCNHjtSiRYtqUlKSqqp+9NFHetddd2lycrKqqu7Zs0f37t3rzRBVVfW3337TkiVL6qZNm7wdyhUdOXJEd+zYoaqqcXFxunnz5kwdl5iYqKqqS5Ys0WLFimmFChV02bJlHovTGG8hmzUYu9EyH9myZQsHDhzg3nvvpWBBz1U+VZXExETi4+NJSEggPj7e9dzHxyfDgRlDQkL48ssvGTduHL6+vuzatYtq1aqlqD15m6rSpEkT/Pz8XGN5nT59mvDwcIoUKULt2rWJi4vjtddeIzw8nIiICNffUaNG8dZbbxEeHs6IESMYN24cVatW9fIrMibnZfdGS0sw+UBcXBxr1qxh9+7dri/+tL7801uXmeep113J8OHDGTduXKYmp1JVmjZtSunSpVm7dm1OvSWZpqqcOnXKlRyCgoJo3LgxPXv2ZN26dbz00ktEREQQERHhGmTy8ccf5+uvvyY5OZny5ctTrlw5qlatSpUqVahSpQp33nkn3bt3z/XXYkxuszv5rwMbNmxI8wvNx8cHX19fChUqhK+vb7rPL/0tWbLkFbdndLyvry/r1q1j/PjxlCtXjrfeeivD2EWE6dOnc/bsWcBx+fOkSZMYPHgwxYsXz/Z7s3v3bg4cOJCihlGxYkXefPNNABo1asTu3btTHHMpwXTs2JEbb7yRmjVrpkggjRs3dr2/1+Jl2Mbkmuy0r2X0AO4E9gChwCtpbC8FLAS2AbuAwc711YGVQIhz/XNux3wM7Aa2Az8BpTOKI7/3wZw8eVIrVaqk3bt319OnT+uFCxdc/QC5LTk5WQcPHqyAjhs37qqPX7RokQJZunrtyJEj+tlnn+mwYcNc6zp37qyA61G2bFnt1auXa/tXX32l7733ns6ePVvXrFmjL7/8sh4+fPiqz23M9Yhs9sF4MrkUAP4GagO+ziTSKNU+rwEfOZ9XAE45962CYypkAD9g76VjgW5AQefzjy4df6VHfk8wkZGRCuSZy4ATEhK0V69eCujMmTOv+vigoCDXhQlff/21fvHFF64LF1KLiIjQcePGaceOHV1JpFWrVhoXF6eqqhs2bNC//vpLDxw4oBcuXLjs+HfffVcrV66s0dHRVx2nMde77CYYTzaRtQNCVXU/gIj8APQCgt32UcBPHI35JZwJJlFVI4AIAFU9IyIhwA1AsKouczs+AHjQg6/B65KSkvjss88AuPnmm70cjUPBggX54YcfuPPOOxk4cCBlypThrrvuyvTxLVu2dD1fvnw5UVFRPPfcc4DjB8+JEycoUaIExYsX58cff+S5556jadOmjB07lj59+qQYzr5du3aXla/OixQKFSpEjx49uHDhAkWKFMnGKzbGZEl2stOVHji++L92W34UGJ9qHz8cTWERwFng7jTKqQkcBkqmsW0h8Eg6538SCAQCb7zxxhzJ5t6wbds2BVRE9OzZs94OJ4WYmBht2bKlFi1aVNetW5flcs6cOaMnT57Uzz77TIsXL64+Pj46depUVXXU3oKDgzNd1sWLF/WOO+7QV199NcvxGGMcyGYNxpNDxaR1iVHqS9a6A1uBqkALYLyIlHQVIFICmAc8r6qxKQoXGQMkAjPTOrmqTlbVNqrapkKFCll9DV7XsGFD2rVrR5MmTXKkUzwnlSpViiVLllCtWjXuvvtudu7cedVlXLhwgd69e1O5cmVeeOEFkpKSePzxx+nYsSNnzpyhZMmSrhtLr8TxfwF8fX1p3LgxtWrVuupYjDE5y5MJJgxHZ/0l1YDwVPsMBuY7k2UocABoACAihXAkl5mqOt/9IBEZCNwDDNBL3yzXqAIFCrB7925uueUWb4eSpooVK7Js2TKKFStGt27dUkwfnJaYmBimT5/ORx99BDjmVylWrBijR49my5YtnD9/nsmTJ1OvXj3eeOMNmjRpkmKO+rTs3LmTDh06uM79xRdfMGTIkJx5gcaYLPNkH8wmoJ6I1AKOAn2B/qn2OQx0Af4UkUpAfWC/s0/mGyBEVT9zP0BE7gReBjqr6nkPxp8nvPbaa8TGxuaZ/pe01KxZk6VLl3LbbbfRrVs31q5dm2IE4djYWBYsWMCPP/7I0qVLiY+Pp1GjRowePRofHx9++umnNMvt3r07FStWdPWfrF+/nnbt2l02gGTp0qWJjY0lPDzcai7G5CXZaV/L6AH0wHEF2N/AGOe6ocBQ5/OqwDJgB7ATZ38K0AlHc9p2HE1oW4Eezm2hwBG39RMziiO/XkUWHh7uunIqJCTE2+Fk6K+//tJixYppixYtNCwsTBMSElRV9dVXX1VAq1Wrpi+88IIGBAS4riLLrAMHDmiBAgVcg0n+8ssvOnLkSNf29K5CM8ZkHXn1MuW89MivCUZVdeDAgVqqVKl88QV67tw5fe2111RE1MfHR3/++WdVdSSHtWvXZus1JCUl6bx58zQ8PFxVVd966y1t1qyZxsbG5kjsxpjLZTfB2FAxeVzz5s2pUqUKS5Ys8XYo6YqOjuaZZ55h4cKFnDt3jpIlSxIbG8s//vEPli1b5pFx0+Lj4xER1wjOxpicl92hYmzCsTzsxRdfZOfOnbRv397boaRw8eJFFi5cyIwZMwDH1WQhISE88sgj/P7775w6dYrx48ezcuVKhgwZgid+xFwawsYYk3fZWGR5VFxcHFOnTiU5OTnPdPDHxcXx/PPP88MPP3D69GkaN27MI488go+PD1u2bEkx+OWwYcOIjIzkrbfeoly5cnz88ceZGhzTGHPtsBpMHlWkSBFGjx4NpH23em5TVZ555hkmTZpEr169WLx4MUFBQa7taSWPN954g+HDh/Ppp5/y73//OzfDNcbkAVaDycM2bNjATTfdRLly5bwdCocOHWLOnDm8/vrrvPPOO5k6RkT48ssviYqK4pVXXqFcuXI88cQTHo7UGJNXWILJo4YOHcoff/zB/fff7+1QAMe9Ltu3b89wsrHUfHx8mDZtGtHR0Tz11FOULVuWBx54wENRGmPyEmsiy4NUleDgYM6cOeP1/pfw8HAmT56MqlKrVi18fK7+I+Pr68vcuXNp3749/fr1Y+XKlR6I1BiT11iCyYNEhGHDhgF49Qqyixcv8uCDD/LCCy8QFhaWrbKKFy/OokWLqFevHvfeey/59bJxY0zmWYLJowICAihatChNmzb1WgzPPfcc69evZ/r06VSvXj3jAzJQtmxZli5dSvny5bnrrrvYs2dPDkRpjMmrLMHkQQMHDuT777+nTZs2XrvXY8qUKUyaNIlXX32V3r1751i5N9xwA8uWLcPHx4du3bplu2ZkjMm7LMHkQWXLliUyMtJr/S/Hjh1jxIgRdO/enbFjx+Z4+fXq1WPJkiXExMTQrVs3oqKicvwcxhjvs6vI8qC+ffvyxRdfeC3BVK5cmZ9//pm2bdteNnJxTmnZsiULFiyge/fu3H333axYsYISJUp45FzGc86ePcu6devYtGkTvr6+lC5dmtKlS1OmTBnX80sPG3nh+mMJJo9JSEhgw4YNQO538MfHx7Nlyxbat29P9+7dPX6+zp07M3v2bB544AF69+7NwoUL8fX19fh5TdbFxsaybt06Vq1axerVqwkMDCQpKSlTxxYvXjzN5OO+nN62kiVLZukKRuNdNthlHvPYY4/x22+/UaRIEY4cOZKr5x4+fDgTJ04kJCSEevXq5dp5p06dyj//+U8efvhhZs6c6bFaU06Jiopizpw5LF68mKpVq9KqVStatWpFkyZNXHPXXCtOnz7N2rVrXQll8+bNJCcnU6hQIdq1a4e/vz+dO3emQ4cOiAgxMTHExMQQHR3tep56Oa1tp0+fvuKYdSJCyZIlM0xGVapUoUGDBtSrV4/ChQvn4jt1bcruYJdWg8lj/P39+fXXX3O9eWzq1KlMmDCBUaNG5WpyARg8eDBRUVGMHj2aMmXK8NVXX+W5ccsuXLjAwoULmTlzJr/99hsJCQnUrl2bP//8k0mTJgFQsGBBGjdu7Eo4rVq1onnz5nluqusriYmJ4c8//3QllC1btpCcnIyvry/t27dnzJgxroRSrFixy44vXrw4N9xww1WfNzk5mTNnzmSYiNyf79u3z7V89uzZFOX5+PhQu3ZtGjZsSIMGDVx/GzRoQJkyZbL8/pir49EajHP2yS+BAsDXqvphqu2lgBnAjTiS3SeqOlVEqgPfAZWBZGCyqn7pPKYsMBuoCRwE+qhq9JXiyE81mOPHj1O5cmU++eQTXnzxxVw5Z2BgIJ06daJTp04sWbLEI8PrZ8Yrr7zCRx99dFXD0XhSUlISq1atYsaMGcybN48zZ85QpUoV+vfvzyOPPELz5s0BOHjwIEFBQa7H5s2bOXnyJOD45d2gQYMUSadFixaULl3ai6/sf06dOpUioWzduhVVpXDhwtx8882uGsrNN99M0aJFvR1uuhISEoiJiSEsLIyQkBB2797t+rt3717i4+Nd+1aqVClF0rn0t3r16nnuh423ZbcG47EEIyIFcMxm2RUIwzGFcj9VDXbb5zWglKq+LCIVgD04kko5oIqqBomIH7AZuE9Vg0Xk38ApVf1QRF4Byqjqy1eKJb8kmKioKFauXMlDDz3E2rVr6dixo8fPGR0dTfPmzfHx8SEwMJDy5ct7/JzpUVWGDBnCN998w5dffsmIESO8EsO2bduYMWMG33//PeHh4fj5+dG7d28eeeQR/P39M2zCU1XCw8NTJJ2goKAUl2TXqVMnRdJp2bIlFSpU8PTLIyoqijVr1rgSyvbt21FVihQpQocOHVwJpX379tdMc19iYiIHDx50JZxLySckJISYmBjXfsWLF6d+/fopajsNGzakbt26121zW15OMB2At1S1u3P5VQBV/cBtn1eB6sAwHDWS5cBNqpqcqqxfgPGqulxE9gD+qhohIlWAVapa/0qx5JcEM2LECKZMmUJiYiKxsbG58otRVfn444/p2rUrLVu29Pj5MpKYmEifPn346aefmDFjBgMGDMiV8x46dIhZs2Yxc+ZMdu3aRcGCBenRowcDBgygZ8+eOfJvceLECbZs2ZIi6ezfv9+1vXr16imSTqtWrahSpUq2flWfPHkyRULZsWMHAEWLFuWWW26hc+fO+Pv7065du+vuS1RVOXHiRIrazqW/hw8fdu1XoEABateunWatJ6/URD0lLyeYB4E7VfUJ5/KjQHtVHe62jx+wAGgA+AEPq+qvqcqpCawBmqhqrIjEqGppt+3RqnpZo6qIPAk8CXDjjTe2PnToUA6/wpy3Zs0annzySUqUKJErQ6mcOnWKsmXLevw8VysuLo4ePXrw559/8ssvv9CjRw+PnCc6Opo5c+YwY8YM/vzzTwA6duzIgAEDeOihh3KlNhcdHc3WrVtTJJ09e/a4OrwrVap0WdKpUaNGuknnxIkTrF692pVQdu3aBUCxYsXo2LGjK6G0bdvWrti7grNnz7J3797Lks++fftSNLdVrlw5RW2nQYMG1K9fn7Jly1K8ePF8f+VbXk4wDwHdUyWYdqr6rNs+DwIdgReAOjhqMM1VNda5vQSwGnhPVec712UqwbjLLzWYpKQkSpcuzcCBAxk/frxHzzVjxgxGjBjBn3/+SePGjT16rqyIjY3l9ttvJzg4mOXLl+dYc2FcXBy//vorM2bMYPHixcTHx9OgQQMeeeQR+vfvT61atXLkPNlx9uxZtm3bliLp7Nq1y3U5cJkyZVIkHIDVq1ezevVqQkJCAEdzT6dOnVwJpXXr1pZQckBiYiIHDhy4LPGEhIRw+vTpFPuKCCVKlKBkyZL4+fll+a+fn5/XklVevoosDEfz1yXVgPBU+wwGPlRHlgsVkQM4ajMbRaQQMA+YeSm5OB0XkSpuTWQnPPcSck9YWBibN2/m7NmzHr+CbMuWLQwZMoSbb76Zm266yaPnyqqSJUvy22+/0alTJ+655x7WrFmT5XHZkpOTWbNmDTNmzGDu3LmcPn2aypUrM2zYMAYMGECrVq3yVOduiRIl6NixY4qkeuHCBXbu3Jki6Xz55ZeuX9MlSpTg1ltvZeDAgfj7+9OqVSu7sdEDChYsSL169ahXrx49e/Z0rb/U3HaplnP69GliY2M5c+bMZX+PHz/uWo6Njc3UfURZSVbVq1fH39/fg+9GxjxZgymIo5O/C3AURyd/f1Xd5bbPf4DjqvqWiFQCgoDmQBQwHUdn/vOpyv0YiHLr5C+rqi9dKZb8UIN54403ePfdd1FV9u3bR926dT1ynsjISNq0aUNSUhKbN2+mYsWKHjlPTjl06BAdO3YkOTmZtWvXUrt27Uwfu337dmbOnMmsWbMICwujRIkSPPDAAzzyyCPcfvvtef5+m4zEx8cTHBxMUlISzZs399rVfybrVJW4uLg0E1FW/ronq9tuu43Vq1dnK77s1mBQVY89gB44kszfwBjnuqHAUOfzqsAyYAewE3jEub4ToMB2YKvz0cO5rRzwO7DP+bdsRnG0bt1a87oDBw7o7bffruXKldPk5GSPnCMhIUG7dOmihQsX1o0bN3rkHJ6wa9cuLVu2rNapU0cjIiKuuO/hw4f1o48+0qZNmyqgBQsW1Lvvvlu///57PXfuXC5FbEzuS05O1vPnz+uxY8d03759Ghoamu0ygUDNRg6wO/nzkCZNmlCjRg1+/fXXjHfOggsXLvD444/TrVs3Bg0a5JFzeMqGDRvo0qULdevWZdWqVSmu3omJiWHevHnMmDGD1atXo6p06NCBAQMG0KdPn1y5/NeYa1Fe7oMxmXTw4EH++usvgoODefjhhz1yDlWlaNGizJw5M0/1N2RW+/btmT9/Pvfccw/33nsvCxYsYOXKlcycOZNFixZx8eJFbrrpJt5++2369+9PnTp1vB2yMdc9SzB5wPz581137Xuig3/79u088cQT/PDDD1fVh5HXdOvWjRkzZtC3b1/Kly9PUlISFStWZOjQoQwYMIA2bdrky+RpzLXKEkwe8Mwzz7Bnzx6mTJlCu3btcrTsU6dOcf/99xMXF5fm2FH5TZ8+fbh48SIrV67k4YcfpkuXLta5bUweZf8z84AiRYpw9OhRGjZsSKlSpXKs3KSkJPr3709YWBirV6+mcuXKOVa2Nz366KM8+uij3g7DGJMBSzBedvjwYb7//nvWr19Pr169crTs119/naVLlzJlyhSvTV5mjLl+WYLxsvXr1/PKK68AOdv/cvHiRZYvX85TTz3FE088kWPlGmNMZlmC8bKHH36YqKgohg0blqMJpnDhwqxZsybfj4VkjMm/7NsnDwgJCaF48eI5MiZYTEwMw4cP5/Tp0xQtWvS6GyHXGJN3WILxohMnTvD444+zcuVK2rVrl+2hS5KTkxkwYACTJk1yDXpojDHeYk1kXhQaGsr8+fM5c+ZMioHzsuqtt95i8eLFfPXVV9apb4zxOqvBeNEtt9zCwoULSUpKynZC+Pnnnxk7diz//Oc/GTp0aA5FaIwxWWcJxss2btwIOIZCyarExEReeukl2rZty4QJE+xudmNMnmBNZF5y7tw5evTogapSs2bNbN0EWbBgQVauXOmaW90YY/KCdGswItLdOeNk6vUDRKSrZ8O69h0/fpz4+Hh2796d5eax5ORk/vvf/5KUlMQNN9xAtWrVcjhKY4zJuis1kb2NY7ri1H4H3vFMONeP2rVrM2/ePE6ePJnl5rF3332Xxx57jAULFuRwdMYYk31XSjDFVPVk6pWqegwo7rmQrh8bNmwAsnYH/6JFi3jzzTd57LHHuO+++3I4MmOMyb4rJZgizmmPUxCRQkDRzBQuIneKyB4RCXVOb5x6eykRWSgi20Rkl4gMdtv2rYicEJGdqY5pISIBIrJVRAJFJGeHH84FiYmJ1K1bl4kTJ+Lr60vLli2v6vi9e/e65pKfOHGideobY/KkKyWY+cAUEXHVVpzPJzq3XZGIFAAmAHcBjYB+ItIo1W7DgGBVbQ74A5+KiK9z2zTgzjSK/jfwtqq2AN5wLucrZ86coVOnToSHh9OyZcuruts+OTmZvn374uvry/z58ylaNFO53hhjct2VEsy/gOPAIRHZLCJBwEHgpHNbRtoBoaq6X1XjgR+A1MMFK+Anjp/gJYBTQCKAqq5xLqemQEnn81JAeCZiyVPKlCnD119/zd9//33VzWM+Pj589dVXzJkzhxo1angoQmOMyb50L1NW1UTgFRF5G6jrXB2qqhcyWfYNwBG35TAgdW/2eGABjiThBzysqskZlPs8sFREPsGRIG9JaycReRJ4EuDGG2/MZMi548KFC+zevZsLFy5cVQf/7t27adCggd2lb4zJF650mfIDIvIAjiauejiSTBsR8ctk2Wl1DGiq5e7AVqAq0AIYLyIlubKngZGqWh0YCXyT1k6qOllV26hqmwoVKmQyZM9TVerWrctLL70EZL6Df9myZTRu3JgffvjBk+EZY0yOuVITWc9Uj3uBUcB2Ebk9E2WHAdXdlqtxeXPWYGC+OoQCB4AGGZQ7kP/1Ac3B0RSXbyQmJjJ8+HBUlYoVK1KzZs0Mj7l48SJDhw6lfv363HvvvZ4P0hhjcsCVmsgGp7VeRGoAP3J5c1dqm4B6IlILOAr0Bfqn2ucw0AX4U0QqAfWB/RmUGw50BlYBtwP7Mtg/TylUqBCvvvoq06dP5+abb87UFWBffvklBw4cYNmyZRQrViwXojTGmOy76qFiVPWQ81LljPZLFJHhwFKgAPCtqu4SkaHO7ROBscA0EdmBo0ntZVWNBBCR73FcWVZeRMKAN1X1G2AI8KXzEuo4nP0s+cXBgwcpUqQIe/bsYeDAgRnuf/z4cd59913uueceuna1ARSMMfnHVScYEWkAXMzMvqq6GFicat1Et+fhQLd0ju2Xzvq1QOvMxpvX9OjRg1KlSgGZ63/ZsWMHhQsX5pNPPvF0aMYYk6PSTTAispDLO+XLAlWARzwZ1LXs3Xff5ZdffmHDhg20adMmw/3vuOMOjhw5YoNYGmPynSvVYFL/ZFYc96WUxZFg1nsqqGvZAw88wJQpU2jSpAl+fulfkKeqLFmyhO7du1tyMcbkS+leRaaqqy89gNPAPcAiHINg2ny8WbB582ZCQ0PZsGFDhs1jCxcupEePHnz//fe5FJ0xxuSsKzWR3YTjyq9+QBQwGxBV/UcuxXbNGTFiBBcuXCA6OvqKCSY+Pp5Ro0bRoEED+vTpk4sRGmNMzrnSfTC7cVxC3FNVO6nq/wFJuRPWtWnKlCncfffdwJU7+L/66iv27dvHp59+SqFCGV6wZ4wxedKVEkxv4BiwUkSmiEgX0r4732RSo0aNiIqKomTJkjRokPb9pFFRUbz99tt069aNu+66K5cjNMaYnHOlGy1/An5yjqB8H45hWSqJyH+An1R1We6EeG1YtWoVFy5cICAggHbt2uHjk3ZuP3z4MJUqVeLTTz+1YfiNMflahvfBqOo5YCYwU0TKAg8BrwCWYK7CJ598wt9//82+fft49dVX092vZcuWBAcHp5uAjDEmv7iqbzFVPaWqk1Q1M2ORGTezZ8/mX//6F0lJSen2v8yYMYPz589bcjHGXBPsmyyXFC9enKNHjwKkOUT/kiVLePTRR/n2229zOzRjjPGIqx4qxly9ZcuWsWvXLtavX0+dOnUoX758iu2JiYm8+OKL1K1blyefzFdDqxljTLosweSCRYsWMXfuXABuv/3y1sXJkycTHBzMTz/9hK+v72XbjTEmP7Imslwwbtw4li9fTkRExGX9L9HR0bzxxhv4+/vTq1fqGaWNMSb/sgSTS4KDg4HLb7CMiYmhadOmfP7553ZZsjHmmmJNZB62ZMkSfv75ZwoWLEiRIkVo1qxZiu21atVi5cqVXorOGGM8x6M1GBG5U0T2iEioiLySxvZSIrJQRLaJyC4RGey27VsROSEiO9M47llnubtE5N+efA3ZFRoayqJFiwgKCqJVq1Yp+lj+7//+j/Dw1LNIG2PMtcFjCUZECgATgLuARkA/EWmUardhQLCqNscxe+WnInLpG3gacGca5f4D6AU0U9XGXD6tQJ4yfPhw/v77b7Zs2ZKieeyPP/5gxIgRfPfdd16MzhhjPMeTNZh2QKiq7lfVeOAHHInBnQJ+4uh8KIFjvplEAFVd41xO7WngQ1W96NzvhIfizzE7duwgLi7OlWCSkpIYOXIkNWrU4Pnnn/ducMYY4yGeTDA3AEfclsOc69yNBxoC4cAO4DlVTc6g3JuAW0Vkg4isFpG2ae0kIk+KSKCIBJ48eTJrryCbVqxYwe23386vv/4K/K+Df+rUqWzfvp1///vfNpmYMeaa5ckEk9YlUamnYO4ObAWqAi2A8SJSMoNyCwJlgJuB0cCPksblV6o6WVXbqGqbChUqXGXoOSMuLo4zZ84QEhJC1apVqVatGrGxsfzrX/+iY8eOPPTQQ16JyxhjcoMnE0wYUN1tuRqOmoq7wcB8dQgFDgBpj2OfstxLx2wEkoHyGRzjFffccw+bNm0iMDCQ9u3bIyLEx8dz55132mXJxphrnicTzCagnojUcnbc9wUWpNrnMI5JzRCRSkB9YH8G5f4M3O485ibAF4jMubBzhqqiqkRGRvL333+7msfKly/PtGnTaNs2zZY9Y4y5ZngswahqIjAcWAqEAD+q6i4RGSoiQ527jQVuEZEdwO/Ay6oaCSAi3wPrgfoiEiYijzuP+Rao7bx8+QdgoKqmbnrzuoCAAKpVq+a6Suzmm2/mvffeIygoyMuRGWNM7vDojZaquhhYnGrdRLfn4UC3dI7tl876eOCRHAzTI4oUKULnzp0JCwujQIECXLx4kX/9618kJCTQqlUrb4dnjDEeJ3nwx3+Oa9OmjQYGBnrl3F27diUqKooCBQoQERHBnj17KF68uFdiMcaYqyEim1W1TVaPt7HIPEBVOX36NMnJyWzcuJEyZcoQGBjIBx98YMnFGHPdsATjAbt376ZMmTKMGzeO2NhYgoKCaNu2LQMGDPB2aMYYk2tssEsP8PPz46233iIhIQGARx99lL59+9pUyMaY64r1wXjQk08+ydy5c4mMjLTkYozJd6wPJg/atm0biYmJ/Pzzz9SqVcuSizHmumTffDksIiKCFi1aMGLECE6ePGmd+saY65YlmBzm5+fHrFmzWL58OYCNlmyMuW5ZgslhJUqUQEQIDQ0FwN/f37sBGWOMl9hVZDnsp59+YtSoUZQsWZIqVapQtmxZb4dkjDFeYTWYHHTmzBl69+5Nq1atKFCgQIoZLI0x5npjNZgcVKxYMTZv3szZs2dZuHChJRhjzHXNEkwOGj16NLVr16ZcuXIAtG/f3ssRGWOM91iCySFBQUF88cUX9O/fn/Lly1O0aFGaNm3q7bCMMcZrrA8mB6iq63Lk4sWLExAQQNu2bSlY0PK3Meb65dEEIyJ3isgeEQkVkVfS2F5KRBaKyDYR2SUig922fSsiJ5wTi6VV9igRURHx+nTJP/30E3/++SeffvopY8aMYcuWLdb/Yoy57nkswYhIAWACcBfQCOgnIo1S7TYMCFbV5oA/8KlzemWAacCd6ZRdHeiKY8plr0pMTGT06NE0btyYZ599lvDwcOLj4y3BGGOue56swbQDQlV1v3MWyh+AXqn2UcBPRAQoAZwCEgFUdY1zOS2fAy85j/eqggULMnXqVLp06cKPP/5IQEAAYB38xhjjyU6CG4AjbsthQOpv3fHAAiAc8AMeVtXkKxUqIvcCR1V1myMvpbvfk8CTADfeeONVB58ZqoqIcNtttzFy5Eiio6NJSEigevXqVK1a1SPnNMaY/MKTCSatb//UNY7uwFbgdqAOsFxE/lTV2DQLFCkGjAG6ZXRyVZ0MTAbHcP2ZDzvzRowYQaFChfj0008JDAwkLi6ORo0aWfOYMcbg2SayMKC623I1HDUVd4OB+eoQChwAGlyhzDpALWCbiBx0lhkkIpVzLOpM2rFjB1999RVJSUmICCLC6dOnOXjwoCUYY4zBszWYTUA9EakFHAX6Av1T7XMY6AL8KSKVgPrA/vQKVNUdQMVLy84k00ZVI3M29CtTVV544QVKlSrFm2++yXvvvcfFixdp3bo1gCUYY4zBgwlGVRNFZDiwFCgAfKuqu0RkqHP7RGAsME1EduBoUnv5UrIQke9xXFlWXkTCgDdV9RtPxXs1fv31V1asWMGXX35J2bJl2b9/P+fPnychIYFChQrRsmVLb4dojDFeZ1MmXyVVpXnz5sTHx7Njxw4KFSrk2nb77bdz5swZNm3alCPnMsYYb8rulMl2q/lVEhF++uknoqKiUiSXpKQkNm7cyODBg69wtDHGXD8swVyFxMREChYsSJ06dahTpw4Ab775Jtu2bePtt9/m3Llz1v9icl1CQgJhYWHExcV5OxSTTxUpUoRq1aql+NGcEyzBXIUXXniBw4cPM3/+fHx8HBfg+fn5UbZsWTZs2ABYB7/JfWFhYfj5+VGzZk2udG+YMWlRVaKioggLC6NWrVo5WrYNdplJISEhfPXVV1SpUsWVXABGjRrFt99+S0BAAOXLl6d27dpejNJcj+Li4ihXrpwlF5MlIkK5cuU8UgO2BJNJo0aNonjx4rzzzjuudUlJSa7nGzZsoH379vaf3HiFfe5Mdnjq82MJJhOWLVvG4sWLef3116lQoYJr/fvvv0+dOnU4fvw4wcHB1jxmjDFuLMFkwgcffECdOnV49tlnU6xv1qwZvXr1Yvv27YD1vxhjjDtLMJnw008/MW/ePAoXLpxifa9evfjss88ICAhARGjbtq2XIjQmb1uwYAEffviht8PIUM2aNYmMzJmBQQYNGsTcuXOzdOzJkydp3749LVu25M8//8xw/4kTJ9K0aVNatGhBp06dCA4OztJ5c5pdRZYJpUuXpnTp0inWnTlzBh8fH9cMlo0aNaJUqVLeCdCYPO7ee+/l3nvv9XYY+cbvv/9OgwYNmD59eqb279+/P0OHDgUcyfyFF15gyZIlngwxU6wGk0XTp0+nVKlSREREuDr4jckL/P39mTZtGuC4R8bf358ZM2YAcP78efz9/Zk9ezYAp0+fxt/fn/nz5wMQGRmJv78/CxcuBODYsWMZnu/gwYM0aNCAJ554giZNmjBgwABWrFhBx44dqVevHhs3bmTatGkMHz4ccPyyHzFiBLfccgu1a9e+4q/8iIgIbrvtNlq0aEGTJk1cv+affvpp2rRpQ+PGjXnzzTdd+9esWZPXXnuNDh060KZNG4KCgujevTt16tRh4sSJAKxatYrbbruN+++/n0aNGjF06FCSky+fJWTGjBm0a9eOFi1a8NRTT5GUlERSUhKDBg2iSZMmNG3alM8//zzD9wdg8+bNdO7cmdatW9O9e3ciIiIAmDJlCm3btqV58+b07t2b8+fPs3XrVl566SUWL15MixYtuHDhQobllyxZ0vX83LlzeeaiD0swWdSxY0fGjh3LuXPniIqKsv4Xc10LDQ3lueeeY/v27ezevZtZs2axdu1aPvnkE95///3L9o+IiGDt2rUsWrSIV165bDZ1l1mzZtG9e3e2bt3Ktm3baNGiBQDvvfcegYGBbN++ndWrV7v6QQGqV6/O+vXrufXWW13NVAEBAbzxxhuufTZu3Minn37Kjh07+Pvvv10J9pKQkBBmz57NunXr2Lp1KwUKFGDmzJls3bqVo0ePsnPnTnbs2JGpkTsSEhJ49tlnmTt3Lps3b+af//wnY8aMAeCBBx5g06ZNbNu2jYYNG/LNN9/QokUL3nnnHR5++GG2bt1K0aJFefjhh2nRosVlj++++851ngkTJlCnTh1eeuklxo0bl2FcucGayLKoZcuWtGzZ0vXL0BKMyStWrVrlel6oUKEUy8WKFUuxXKpUqRTL5cuXT7FcuXLmZsKoVasWTZs2BaBx48Z06dIFEaFp06YcPHjwsv3vu+8+fHx8aNSoEcePH0+33LZt2/LPf/6ThIQE7rvvPleC+fHHH5k8eTKJiYlEREQQHBxMs2bNAFxNcU2bNuXs2bP4+fnh5+dHkSJFiImJAaBdu3aue9b69evH2rVrefDBB13n/f3339m8ebOrX/XChQtUrFiRnj17sn//fp599lnuvvtuunXLcGoq9uzZw86dO+natSvguL2hSpUqAOzcuZN//etfxMTEcPbsWbp3755mGZdqnFcybNgwhg0bxqxZs3j33Xcz3bzmSZZgsiA6OpqwsDAaN25MQEAAJUqUoFGjRt4Oyxivcb8AxsfHx7Xs4+NDYmLiFfe/0oC7t912G2vWrOHXX3/l0UcfZfTo0dx666188sknbNq0iTJlyjBo0KAUNwm6nzt1XJdiSd2ElHpZVRk4cCAffPDBZTFt27aNpUuXMmHCBH788Ue+/fbbdOO/VFbjxo1Zv379ZdsGDRrEzz//TPPmzZk2bVqK5O7u4YcfZs+ePZetf+GFF3jsscdSrOvbty9PP/30FWPKLdZElgWLFi2iWbNm7Nixg4CAANq1a0eBAgW8HZYx15xDhw5RsWJFhgwZwuOPP05QUBCxsbEUL16cUqVKcfz4cX777berLnfjxo0cOHCA5ORkZs+eTadOnVJs79KlC3PnzuXEiRMAnDp1ikOHDhEZGUlycjK9e/dm7NixBAUFZXiu+vXrc/LkSVeCSUhIYNeuXYDjYqEqVaqQkJDAzJkz0y1j9uzZbN269bLHpeSyb98+176//vor9erVu7o3xEOsBpMFXbt25bvvvqNOnTps27aN0aNHezskY65Jq1at4uOPP6ZQoUKUKFGC7777jlq1atGyZUsaN25M7dq16dix41WX26FDB1555RV27Njh6vB316hRI9599126detGcnIyhQoVYsKECRQtWpTBgwe7LgpIq4aTmq+vL3PnzmXEiBGcPn2axMREnn/+eRo3bszYsWNp3749NWrUoGnTppw5c+aqXwvA+PHjWbFiBYUKFaJMmTJ5onkMcFTfPPUA7gT2AKHAK2lsLwUsBLYBu4DBbtu+BU4AO1Md8zGwG9gO/ASUziiO1q1bqyesXbtWAf3ll188Ur4xmREcHOztEPKVlStX6t133+3tMPKctD5HQKBmIwd4rIlMRAoAE4C7gEZAPxFJ3VExDAhW1eY4Zq/8VER8ndum4UhQqS0HmqhqM2Av8GrOR5++06dPM3fuXGJiYggICACwS5SNMSYNnmwiaweEqup+ABH5AegFuN9iqoCfOHrYSgCngEQAVV0jIjVTF6qqy9wWA4AHU+/jSX/++ScPPfQQq1evJiAggFq1alGpUqXcDMGYa86OHTt49NFHU6wrXLiwaxqMnOTv74+/v3+OlTds2DDWrVuXYt1zzz1nkw/i2QRzA3DEbTkMSP1TfzywAAgH/ICHVfXyO57S908g4+v3clD37t0JCAigefPmBAQEcOutt+bm6Y25JjVt2pStW7d6O4wsmTBhgrdDyLM8eRVZWreSpr4esTuwFagKtADGi0hJMkFExuCo7aR56YWIPCkigSISePLkyczGnKFChQrRvn171wQ9dv+LMcakzZMJJgyo7rZcDUdNxd1gYL6zPykUOAA0yKhgERkI3AMMcHZEXUZVJ6tqG1Vt4z7EfnbExcXx3nvvERoa6qq6W/+LMcakzZMJZhNQT0RqOTvu++JoDnN3GOgCICKVgPrA/isVKiJ3Ai8D96rq+RyP+gou3XW7a9cuAgIC8PX1dd1ZbIwxJiWPJRhVTQSGA0uBEOBHVd0lIkNFZKhzt7HALSKyA/gdeFlVIwFE5HtgPVBfRMJE5HHnMeNx9NcsF5GtIjLRU68htTZt2hAVFUW3bt0ICAigVatWlw3hb4wxxsGjd/Kr6mJVvUlV66jqe851E1V1ovN5uKp2U9WmqtpEVWe4HdtPVauoaiFVraaq3zjX11XV6qrawvkYmvbZPaNs2bIULFiQwMBA638xJpNsPpirc7XzwUybNo0KFSq4BsH8+uuvs3TenGZ38mdScnIyQ4cO5ZFHHqFEiRJcuHDBEozJk9K6BLdPnz4888wznD9/nh49ely2fdCgQQwaNIjIyMgUgz4C6Y6PdTVsPpirc7XzwYBjvLLx48d7MKqrZ2ORZdLRo0f55ZdfOHjwoHXwG+PG5oPJmKfng8mzsjMMQH555NRQMcnJyZqQkKCPPfaYVqpUSZOTk3OkXGOyw9tDxRw4cEALFCig27dv16SkJG3VqpUOHjxYk5OT9eeff9ZevXrp1KlTddiwYaqqOnDgQH3wwQc1KSlJd+3apXXq1Em37E8++UTfffddVVVNTEzU2NhYVVWNiopyrevcubNu27ZNVVVr1KihX331laqqPv/889q0aVONjY3VEydOaIUKFVTVMVRM4cKF9e+//9bExES94447dM6cOa7jT548qcHBwXrPPfdofHy8qqo+/fTTOn36dA0MDNQ77rjDFV90dHS6sQ8cOFDnzJmj8fHx2qFDBz1x4oSqqv7www86ePBgVVWNjIx07T9mzBgdN26cqmqK90tVtU+fPtq8efPLHtOnT3ftX7lyZW3atKn27t1bDx8+nG5c6fHEUDHWRHYVRISCBQsSEBDAzTffnGdmjTPG22w+mPTlxnwwPXv2pF+/fhQuXJiJEycycOBA/vjjjwxj8zRLMJl033330bt3b3r06MHevXttGAhj3Nh8MOlT9fx8MOXKlXOtGzJkCC+//PIVY8otlmAy4dy5c5w6dYpz586xceNGwGawNCY3HDp0iBtuuIEhQ4Zw7tw5goKCaN68+WXzwVzt2GKX5oOpUaMGs2fP5sknn0yxvUuXLvTq1YuRI0dSsWJFTp06xZkzZyhevDi+vr707t2bOnXqMGjQoAzP5T4fTIcOHUhISGDv3r00btz4svlgbrjhhjTLyKgGExER4aoVLViwgIYNG2bujfAwSzCZULx4cdasWQPAW2+9hY+PD23atPFyVMZc+2w+mMwZN24cCxYsoGDBgpQtW5Zp06ZlqZycJleqnl4r2rRpo4GBgTlS1p133klERATbtm3LkfKMya6QkJA884s1P1i1ahWffPIJixYt8nYoeUpanyMR2ayqWf41bZcpZ8Ltt9/OG2+8QXJyMhs2bLDmMWOMyQRrIsuAqlKvXj2qVq3K3r17iYmJsQRjTA6z+WCuTZZgMiAiTJo0CcDVrmkJxpicZfPBXJusiewqBAQEUKpUKerXr+/tUIwxJs+zBHMVNmzYQLt27fDxsbfNGGMyYt+UmXTu3Dm2b99uzWPGGJNJlmAyKTAwkOTkZEswxhiTSZZgMikgIACwEZSNyQqbD+bqXO18MGvWrKFVq1YULFjwsnNOnz6devXqUa9evasa/j8nePQqMuf0xl8CBYCvVfXDVNtLATOAG52xfKKqU53bvgXuAU6oahO3Y8oCs4GawEGgj6pGe/J1gCPB1KtXL8WYP8bkNc8//3yOX43VokULvvjii2yVYfPBXJ2rnQ/mxhtvZNq0aXzyyScp1p86dYq3336bwMBARITWrVtz7733UqZMGU+EfRmP1WBEpAAwAbgLaAT0E5FGqXYbBgSranPAH/hURHyd26YBd6ZR9CvA76paD8c0y6/kfPQpqSoBAQFWezEmDTYfTMY8PR9MzZo1adas2WUXIC1dupSuXbtStmxZypQpQ9euXVmyZEmmYs4JnqzBtANCVXU/gIj8APQCgt32UcBPHEOZlgBOAYkAqrpGRGqmUW4vHMkIYDqwCvDo0KFHjhzh2LFj1v9i8rzs1jSyKjQ0lDlz5jB58mTatm3LrFmzWLt2LQsWLOD999/nvvvuS7F/REQEa9euZffu3dx7772XzaJ5yaxZs+jevTtjxowhKSmJ8+fPA/Dee+9RtmxZkpKS6NKlC9u3b3cN11+9enXWr1/PyJEjGTRoEOvWrSMuLo7GjRszdKhjhvWNGzcSHBxMjRo1uPPOO5k/f36KGEJCQpg9ezbr1q2jUKFCPPPMM8ycOZPGjRtz9OhRdu7cCeAa/v9KEhISePbZZ/nll1+oUKECs2fPZsyYMXz77bc88MADDBkyBIB//etffPPNNzz77LO88847BAYGumaozGg05fQcPXqU6tWru5arVavG0aNHM4w5p3gywdwAHHFbDgNSVwHGAwuAcMAPeFhVL/8pkVIlVY0AUNUIEamY1k4i8iTwJDiqj9lxqf/FEowxabP5YNKXG/PBpCetsSZzcx4rTyaYtF5F6lfbHdgK3A7UAZaLyJ+qGpvdk6vqZGAyOAa7zE5ZAQEBFClSxPUBNsakZPPBpC835oNJT7Vq1VKUGRYWlqPD5GTEk1eRhQHV3Zar4aipuBsMzHfOzhkKHAAaZFDucRGpAuD8eyKH4k1XQEAAbdq0oVChQp4+lTHGzaFDh6hYsSJDhgzh8ccfJygoiNjY2Mvmg7lal+aDSU5OZvbs2XTq1CnF9i5dujB37lxOnHB8vZw6dYpDhw4RGRlJcnIyvXv3ZuzYsQQFBWV4Lvf5YMDRZLZr1y6Ay+aDSc/s2bPZunXrZY8rJReA7t27s2zZMqKjo4mOjmbZsmXp1pI8wZM1mE1APRGpBRwF+gL9U+1zGOgC/CkilYD6wP4Myl0ADAQ+dP79JSeDTi0+Pp6goCBXB6UxJvfYfDCZs2nTJu6//36io6NZuHAhb775Jrt27aJs2bK8/vrrrqa+N954g7Jly2bpHFmiqh57AD2AvcDfwBjnuqHAUOfzqsAyYAewE3jE7djvgQggAUdt6HHn+nI4rh7b5/xbNqM4WrdurVl18uRJHTBggC5ZsiTLZRjjScHBwd4OIV9ZuXKl3n333d4OI89J63MEBGo2coBH74NR1cXA4lTrJro9DwfS7CVT1X7prI/CUevJFeXLl2fGjBm5dTpjjLlm2HD9xhivs/lgrk2WYIy5Bqhqrl5+mtNsPhjv0itcyZcdNhaZMflckSJFiIqK8tiXhLm2qSpRUVEUKVIkx8u2Gowx+Vy1atUICwvj5MmT3g7F5FNFihShWrVqOV6uJRhj8rlChQpRq1Ytb4dhzGWsicwYY4xHWIIxxhjjEZZgjDHGeIRcD1eeiMgZ4PKR4kxWlQdyZto/Y+9lzrL3M2fVV1W/rB58vXTy71HVNt4O4lohIoH2fuYMey9zlr2fOUtEArNzvDWRGWOM8QhLMMYYYzziekkwk70dwDXG3s+cY+9lzrL3M2dl6/28Ljr5jTHG5L7rpQZjjDEml1mCMcYY4xHXdIIRkTtFZI+IhIrIK96OJz8SkYMiskNEtl66ZFFEyorIchHZ5/xbxttx5lUi8q2InBCRnW7r0n3/RORV5+d1j4jk3uTp+UQ67+dbInLU+RndKiI93LbZ+5kOEakuIitFJEREdonIc871Ofb5vGYTjIgUACYAdwGNgH4i0si7UeVb/1DVFm73F7wC/K6q9XBMW23JO33TgDtTrUvz/XN+PvsCjZ3HfOX8HJv/mcbl7yfA587PaAvnTLr2fmYsEXhRVRsCNwPDnO9Zjn0+r9kEA7QDQlV1v6rGAz8Avbwc07WiFzDd+Xw6cJ/3QsnbVHUNcCrV6vTev17AD6p6UVUPAKE4PsfGKZ33Mz32fl6BqkaoapDz+RkgBLiBHPx8XssJ5gbgiNtymHOduToKLBORzSLypHNdJVWNAMeHFKjotejyp/TeP/vMZt1wEdnubEK71KRj72cmiUhNoCWwgRz8fF7LCSat+WPtmuyr11FVW+FoahwmIrd5O6BrmH1ms+Y/QB2gBRABfOpcb+9nJohICWAe8Lyqxl5p1zTWXfH9vJYTTBhQ3W25GhDupVjyLVUNd/49AfyEo0p8XESqADj/nvBehPlSeu+ffWazQFWPq2qSqiYDU/hfs429nxkQkUI4kstMVZ3vXJ1jn89rOcFsAuqJSC0R8cXRObXAyzHlKyJSXET8Lj0HugE7cbyPA527DQR+8U6E+VZ6798CoK+IFBaRWkA9YKMX4stXLn0ZOt2P4zMK9n5ekYgI8A0QoqqfuW3Ksc/nNTuasqomishwYClQAPhWVXd5Oaz8phLwk+NzSEFglqouEZFNwI8i8jhwGHjIizHmaSLyPeAPlBeRMOBN4EPSeP9UdZeI/AgE47jCZ5iqJnkl8DwqnffTX0Ra4GiuOQg8BfZ+ZkJH4FFgh4hsda57jRz8fNpQMcYYYzziWm4iM8YY40WWYIwxxniEJRhjjDEeYQnGGGOMR1iCMcYY4xGWYMw1T0RURD51Wx4lIm/lUNnTROTBnCgrg/M85Bz1dmWq9TVFpL+nz29MVliCMdeDi8ADIlLe24G4u8qRfR8HnlHVf6RaXxNIM8GIyDV7n5vJHyzBmOtBIo65xUem3pC6BiIiZ51//UVktYj8KCJ7ReRDERkgIhud8+PUcSvmDhH507nfPc7jC4jIxyKyyTkI41Nu5a4UkVnAjjTi6ecsf6eIfORc9wbQCZgoIh+nOuRD4FbnPCgjRWSQiMwRkYU4Bikt7hwAcpOIbBGRXhnEV0VE1jjL2ykit2bxPTfm2r2T35hUJgDbReTfV3FMc6AhjuHh9wNfq2o7cUzM9CzwvHO/mkBnHAMurhSRusBjwGlVbSsihYF1IrLMuX87oIlzyHMXEakKfAS0BqJxJIj7VPUdEbkdGKWqgalifMW5/lJiGwR0AJqp6ikReR/4Q1X/KSKlgY0isgIYkE58DwBLVfU9Zw2r2FW8X8akYAnGXBdUNVZEvgNGABcyedimS8OWi8jfwKUEsQNwb6r60TnQ4j4R2Q80wDFuWzO32lEpHGM3xQMbUycXp7bAKlU96TznTOA24OdMxnvJclW9NGdKN+BeERnlXC4C3HiF+DYB3zoHQfxZVbde5bmNcbEEY64nXwBBwFS3dYk4m4qdg//5um276PY82W05mZT/d1KPt6Q4hjZ/VlWXum8QEX/gXDrxpTUcela4ly9Ab1XdkyqONONzbrsNuBv4r4h8rKrf5VBc5jpjfTDmuuH8Vf8jjg7zSw7iaJICx4x9hbJQ9EMi4uPsl6kN7MExyOrTzpoAInKTc0TqK9kAdBaR8s7mqX7A6gyOOQP4XWH7UuBZZ0JBRFq6rb8sPhGpAZxQ1Sk4RtptlcH5jUmX1WDM9eZTYLjb8hTgFxHZiGP+8fRqF1eyB0ciqAQMVdU4EfkaR99MkPPL/SQZTC2tqhEi8iqwEkfNY7GqZjQVwnYgUUS24ZivPjrV9rE4am7bnXEcBO4B0ovPHxgtIgnAWRx9ScZkiY2mbIwxxiOsicwYY4xHWIIxxhjjEZZgjDHGeIQlGGOMMR5hCcYYY4xHWIIxxhjjEZZgjDHGeMT/A//DObtCbA6iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "num_trees = list(range(10, 201, 20))\n",
    "\n",
    "plt.plot(num_trees, all_aucs[3], label='min_samples_leaf=3', color='black', linestyle='dotted')\n",
    "plt.plot(num_trees, all_aucs[5], label='min_samples_leaf=5', color='black', linestyle='dashed')\n",
    "plt.plot(num_trees, all_aucs[10], label='min_samples_leaf=10', color='black', linestyle='solid')\n",
    "    \n",
    "plt.xticks(range(0, 201, 50))\n",
    "plt.legend()\n",
    "\n",
    "plt.title('Number of trees vs AUC')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_random_forest_n_estimators_sample_leaf.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the final model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, min_samples_leaf=5, n_estimators=200,\n",
       "                       random_state=1)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=200, max_depth=10, min_samples_leaf=5, random_state=1)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8253826499721489"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf = rf.predict_proba(X_val)[:, 1]\n",
    "roc_auc_score(y_val, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_val, y_pred_rf)\n",
    "plt.plot(fpr, tpr, color='black')\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_val, y_pred_dt)\n",
    "plt.plot(fpr, tpr, color='black', linestyle='dashed')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='black', lw=0.7, linestyle='dashed', alpha=0.5)\n",
    "\n",
    "plt.xlim([-0.02, 1.02])\n",
    "plt.ylim([-0.02, 1.02])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.title('ROC curve')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_train, label=y_train, feature_names=dv.feature_names_)\n",
    "dval = xgb.DMatrix(X_val, label=y_val, feature_names=dv.feature_names_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "xgb_params = {\n",
    "    'eta': 0.3,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'nthread': 8,\n",
    "    'seed': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:29:14] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "model = xgb.train(xgb_params, dtrain, num_boost_round=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08926772, 0.0468099 , 0.09692743, 0.17261842, 0.05435968,\n",
       "       0.12576081, 0.08033007, 0.61870354, 0.486538  , 0.04056795],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(dval)\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8152745150274878"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "watchlist = [(dtrain, 'train'), (dval, 'val')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = {\n",
    "    'eta': 0.3,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.86300\tval-auc:0.76818\n",
      "[10]\ttrain-auc:0.95002\tval-auc:0.81558\n",
      "[20]\ttrain-auc:0.97316\tval-auc:0.81775\n",
      "[30]\ttrain-auc:0.98772\tval-auc:0.81788\n",
      "[40]\ttrain-auc:0.99456\tval-auc:0.81387\n",
      "[50]\ttrain-auc:0.99688\tval-auc:0.81128\n",
      "[60]\ttrain-auc:0.99889\tval-auc:0.80801\n",
      "[70]\ttrain-auc:0.99944\tval-auc:0.80732\n",
      "[80]\ttrain-auc:0.99985\tval-auc:0.80677\n",
      "[90]\ttrain-auc:0.99991\tval-auc:0.80637\n",
      "[99]\ttrain-auc:0.99997\tval-auc:0.80546\n"
     ]
    }
   ],
   "source": [
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=100,\n",
    "                  evals=watchlist, verbose_eval=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To capture this output, we can use \n",
    "\n",
    "- `%%capture` instruction that saves the result to `output`\n",
    "- `parse_xgb_output` function that parses it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=100,\n",
    "                  evals=watchlist, verbose_eval=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_xgb_output(output):\n",
    "    tree = []\n",
    "    aucs_train = []\n",
    "    aucs_val = []\n",
    "\n",
    "    for line in output.stdout.strip().split('\\n'):\n",
    "        it_line, train_line, val_line = line.split('\\t')\n",
    "\n",
    "        it = int(it_line.strip('[]'))\n",
    "        train = float(train_line.split(':')[1])\n",
    "        val = float(val_line.split(':')[1])\n",
    "\n",
    "        tree.append(it)\n",
    "        aucs_train.append(train)\n",
    "        aucs_val.append(val)\n",
    "\n",
    "    return tree, aucs_train, aucs_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use it for plotting the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree, aucs_train, aucs_val = parse_xgb_output(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(tree, aucs_train, color='black', linestyle='dashed', label='Train AUC')\n",
    "plt.plot(tree, aucs_val, color='black', linestyle='solid', label='Validation AUC')\n",
    "plt.xticks(range(0, 101, 25))\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('XGBoost: number of trees vs AUC')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_xgb_default.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter tuning: eta\n",
    "\n",
    "First, we try to set eta to 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.3,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.81788\n",
      "(0.81788, 30)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_eta_03 = parse_xgb_output(output)\n",
    "print(max(aucs_val_eta_03))\n",
    "print(max(zip(aucs_val_eta_03, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.82476\n",
      "(0.82476, 70)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_eta_01 = parse_xgb_output(output)\n",
    "print(max(aucs_val_eta_01))\n",
    "print(max(zip(aucs_val_eta_01, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.05,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.82297\n",
      "(0.82297, 120)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_eta_005 = parse_xgb_output(output)\n",
    "print(max(aucs_val_eta_005))\n",
    "print(max(zip(aucs_val_eta_005, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.01,\n",
    "    'max_depth': 6,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.82168\n",
      "(0.82168, 499)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_eta_001 = parse_xgb_output(output)\n",
    "print(max(aucs_val_eta_001))\n",
    "print(max(zip(aucs_val_eta_001, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(tree, aucs_val_eta_03, color='black', linestyle='solid', label='eta=0.3')\n",
    "plt.plot(tree, aucs_val_eta_01, color='black', linestyle='dashed', label='eta=0.1')\n",
    "# plt.plot(tree, aucs_val_eta_005, color='grey', linestyle='solid', label='eta=0.05')\n",
    "# plt.plot(tree, aucs_val_eta_001, color='grey', linestyle='dashed', label='eta=0.01')\n",
    "\n",
    "plt.xticks(range(0, 501, 100))\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('The effect of eta on model performance')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC (validation)')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_xgb_eta.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(tree, aucs_val_eta_01, color='grey', linestyle='dashed', label='eta=0.1')\n",
    "plt.plot(tree, aucs_val_eta_005, color='black', linestyle='solid', label='eta=0.05')\n",
    "plt.plot(tree, aucs_val_eta_001, color='black', linestyle='dashed', label='eta=0.01')\n",
    "\n",
    "plt.xticks(range(0, 501, 100))\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('The effect of eta on model performance')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC (validation)')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_xgb_eta_2.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter tuning: max_depth\n",
    "\n",
    "First, try 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83657\n",
      "(0.83657, 180)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_depth3 = parse_xgb_output(output)\n",
    "print(max(aucs_val_depth3))\n",
    "print(max(zip(aucs_val_depth3, tree)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, try 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 10,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.81878\n",
      "(0.81169, 100)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_depth10 = parse_xgb_output(output)\n",
    "print(max(aucs_val))\n",
    "print(max(zip(aucs_val_depth10, tree)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With depth=3 it's better than depth=6 and depth=10. So let's try 4 to see if it's better than 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 4,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83014\n",
      "(0.83014, 80)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_depth4 = parse_xgb_output(output)\n",
    "print(max(aucs_val_depth4))\n",
    "print(max(zip(aucs_val_depth4, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(tree, aucs_val_depth3, color='black', linestyle='dashed', label='max_depth=3')\n",
    "plt.plot(tree, aucs_val_depth4, color='grey', linestyle='dashed', label='max_depth=4')\n",
    "plt.plot(tree, aucs_val_eta_01, color='black', linestyle='solid', label='max_depth=6')\n",
    "plt.plot(tree, aucs_val_depth10, color='grey', linestyle='solid', label='max_depth=10')\n",
    "\n",
    "plt.ylim(0.75, 0.845)\n",
    "plt.xlim(-10, 510)\n",
    "plt.xticks(range(0, 501, 100))\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('The effect of max_depth on model performance')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC (validation)')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_xgb_depth.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not better, so we'll leave it at 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune \"min_child_weight\"\n",
    "\n",
    "First, the default value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83657\n",
      "(0.83657, 180)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_mcw1 = parse_xgb_output(output)\n",
    "print(max(aucs_val_mcw1))\n",
    "print(max(zip(aucs_val_mcw1, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 10,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83336\n",
      "(0.83336, 150)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_mcw10 = parse_xgb_output(output)\n",
    "print(max(aucs_val_mcw10))\n",
    "print(max(zip(aucs_val_mcw10, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 30,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83543\n",
      "(0.83543, 160)\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val_mcw30 = parse_xgb_output(output)\n",
    "print(max(aucs_val_mcw30))\n",
    "print(max(zip(aucs_val_mcw30, tree)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(tree, aucs_val_mcw1, color='black', linestyle='solid', label='min_child_weight=1')\n",
    "plt.plot(tree, aucs_val_mcw10, color='grey', linestyle='solid', label='min_child_weight=10')\n",
    "plt.plot(tree, aucs_val_mcw30, color='black', linestyle='dashed', label='min_child_weight=30')\n",
    "\n",
    "plt.ylim(0.82, 0.84)\n",
    "plt.xlim(0, 510)\n",
    "plt.xticks(range(0, 501, 100))\n",
    "plt.yticks(np.linspace(0.82, 0.84, 5))\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('The effect of min_child_weight on model performance')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC (validation)')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_xgb_mcw.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the best number of trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=500, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.77424\tval-auc:0.73571\n",
      "[10]\ttrain-auc:0.84733\tval-auc:0.78964\n",
      "[20]\ttrain-auc:0.86359\tval-auc:0.80617\n",
      "[30]\ttrain-auc:0.87855\tval-auc:0.81478\n",
      "[40]\ttrain-auc:0.88701\tval-auc:0.81990\n",
      "[50]\ttrain-auc:0.89458\tval-auc:0.82595\n",
      "[60]\ttrain-auc:0.90159\tval-auc:0.82807\n",
      "[70]\ttrain-auc:0.90650\tval-auc:0.82801\n",
      "[80]\ttrain-auc:0.91110\tval-auc:0.82940\n",
      "[90]\ttrain-auc:0.91428\tval-auc:0.82997\n",
      "[100]\ttrain-auc:0.91801\tval-auc:0.83221\n",
      "[110]\ttrain-auc:0.92205\tval-auc:0.83290\n",
      "[120]\ttrain-auc:0.92460\tval-auc:0.83285\n",
      "[130]\ttrain-auc:0.92843\tval-auc:0.83414\n",
      "[140]\ttrain-auc:0.93100\tval-auc:0.83463\n",
      "[150]\ttrain-auc:0.93296\tval-auc:0.83525\n",
      "[160]\ttrain-auc:0.93551\tval-auc:0.83554\n",
      "[170]\ttrain-auc:0.93788\tval-auc:0.83638\n",
      "[180]\ttrain-auc:0.93971\tval-auc:0.83657\n",
      "[190]\ttrain-auc:0.94203\tval-auc:0.83562\n",
      "[200]\ttrain-auc:0.94334\tval-auc:0.83512\n",
      "[210]\ttrain-auc:0.94501\tval-auc:0.83546\n",
      "[220]\ttrain-auc:0.94693\tval-auc:0.83508\n",
      "[230]\ttrain-auc:0.94839\tval-auc:0.83417\n",
      "[240]\ttrain-auc:0.94999\tval-auc:0.83336\n",
      "[250]\ttrain-auc:0.95088\tval-auc:0.83341\n",
      "[260]\ttrain-auc:0.95235\tval-auc:0.83379\n",
      "[270]\ttrain-auc:0.95333\tval-auc:0.83389\n",
      "[280]\ttrain-auc:0.95470\tval-auc:0.83288\n",
      "[290]\ttrain-auc:0.95576\tval-auc:0.83274\n",
      "[300]\ttrain-auc:0.95733\tval-auc:0.83173\n",
      "[310]\ttrain-auc:0.95886\tval-auc:0.83222\n",
      "[320]\ttrain-auc:0.95972\tval-auc:0.83201\n",
      "[330]\ttrain-auc:0.96091\tval-auc:0.83139\n",
      "[340]\ttrain-auc:0.96172\tval-auc:0.83101\n",
      "[350]\ttrain-auc:0.96298\tval-auc:0.83059\n",
      "[360]\ttrain-auc:0.96416\tval-auc:0.83034\n",
      "[370]\ttrain-auc:0.96539\tval-auc:0.82945\n",
      "[380]\ttrain-auc:0.96669\tval-auc:0.82905\n",
      "[390]\ttrain-auc:0.96785\tval-auc:0.82876\n",
      "[400]\ttrain-auc:0.96876\tval-auc:0.82846\n",
      "[410]\ttrain-auc:0.96983\tval-auc:0.82744\n",
      "[420]\ttrain-auc:0.97055\tval-auc:0.82764\n",
      "[430]\ttrain-auc:0.97115\tval-auc:0.82695\n",
      "[440]\ttrain-auc:0.97213\tval-auc:0.82659\n",
      "[450]\ttrain-auc:0.97303\tval-auc:0.82662\n",
      "[460]\ttrain-auc:0.97357\tval-auc:0.82642\n",
      "[470]\ttrain-auc:0.97421\tval-auc:0.82591\n",
      "[480]\ttrain-auc:0.97497\tval-auc:0.82635\n",
      "[490]\ttrain-auc:0.97576\tval-auc:0.82604\n",
      "[499]\ttrain-auc:0.97642\tval-auc:0.82599\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83657\n"
     ]
    }
   ],
   "source": [
    "tree, _, aucs_val = parse_xgb_output(output)\n",
    "print(max(aucs_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.83657, 180)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(zip(aucs_val, tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bP/zhDyxatIhHHnmEWrVqRR3ifmnTpg2TJ0/m8ssv589//jNdu3Zl7dq1UYflXImQyiQyCWguqYmkisAVwKi4Mt8DZwBIqgMcBSySVFvSQeHyKsCZwNzwe92Y7S8CZqbwHFwBli9fzgMPPEDjxo3p1q0b06ZN495772XhwoUMHDiQww47LOoQi0z16tV57bXXGDx4MOPGjaNdu3ZMnDgx6rCci16iS5X9eQFdgfkEd2ndEy7rDfQOP9cDPgVmECSD34TL2wD/Jej3mAn8JWaf/xeWn06QlOomisP7RIrW/PnzrXv37la+fHkD7Oyzz7aRI0faTz/9VKxxLFu2rFiPl2vKlCnWrFkzK1++vD300EPFft7OFReSaM5SUK5sy8jIsMmTJ0cdRpnw3Xff0aFDB7Zu3cpNN93EDTfcQNOmTSOJZf78+bRo0SKSY2/evJmbbrqJN998k4yMDF5++WVat25d5MfZtm0ba9eupXr16qW+WdCVPpIyzSyjoDI+Yt0lbfXq1Zx55pls3bqVL7/8kgEDBkSWQAA++OCDyI5do0YN3njjDd566y0WL15Mu3btePTRR8nOzt7nfW3bto2RI0dy00030bVrV9q3b0/jxo2pWrUqVatWpXHjxjRo0IB33303BWfi3P7xCRhdUjZs2MBZZ53FihUr+Pzzz2nTpk3UIZUIl112GZ06deLmm2/mzjvvZOTIkQwfPpyWLVsWuN26desYPXo0I0eO5NNPP2X79u3UrFmTpk2bcthhh9GqVStq16695zV06FAuueQS+vfvz913351wShjniosnEZfQjz/+yHnnncfcuXMZPXo0J510UtQhlSh16tThn//8J2+99RZ9+vShbdu23HvvvTRr1owff/yRbdu28eOPP+75PGXKFMaPH09OTg4NGjTguuuu46KLLqJjx44ccMABeR7j6quv5vrrr+fPf/4zc+bMYejQoVSuXLmYz9S5PCTqNCkLL+9YL7wdO3ZYly5drFy5cvbuu+9GHU6Jt3LlSuvWrdue6VTiXwceeKAdffTRdvfdd9ukSZNs9+7dSe979+7d1r9/fwPsxBNP9FH0LuXwjvWAd6wXTnZ2NpdffjkjRozgpZde4tprr406pF8YO3Ysp512WtRh7MXMmDt3LmZG1apVOfDAA6latSqVK1cukmedvPvuu/z2t7/l0EMP5YMPPuDYY48tgqid21syHevenJXGNm/ezF//+lfGjRtHlSpVfvELr2rVqixcuJCPP/6Yp59+usQlEIApU6aUyCQiiVatWqVs/xdffDFNmjThwgsv5JRTTuGKK66gUaNGe14NGzakYcOG3tzlioUnkTSUk5PDSy+9xD333MOaNWvo2LEjP/30EytWrPhF2/2uXbt45JFH6Nu3b9QhuzjHHXcc3377LTfeeCMffvghq1at2qtM8+bNefXVVznhhDwndXCuSHgSSTPjxo3jtttuY+rUqZxyyimMHj2a9u3bRx1WodSvXz/qECJVr169Pc+W37lzJ8uXL+f777/n+++/Z+nSpQwdOpROnToxfPhwLr/88oijdWVWok6TsvDyjnWzhQsXWo8ePQywRo0a2ZtvvrlPnbqu9FmzZo116NDBALvvvvuS+vfevHmz/1y4PYh4AkZXQsyZM4djjjmGMWPG0L9/f+bOncvll19e6scavPbaa1GHUKLVrl2bzz//nGuvvZYHHniAK664gu3bt+9Vbvv27bzyyiucfPLJ1KhRg0MOOYROnTrRp08fnnvuOSZOnMjGjRuL/wRcqeDNWWng7rvvpkKFCsycOZNGjRpFHU6RyasfwP1SpUqVGDZsGK1bt+aOO+5g8eLFvPfee9SrV4958+bx/PPPM3z4cDZs2ECLFi249957WbNmDTNmzODVV19l8+bNe/aVkZHB/fffT9euXUv9HyCu6HgSKeO+/vpr3nvvPfr371+mEohLniT+9Kc/cdRRR3HVVVdxwgkn0KJFC8aOHUuFChXo0aMHvXv3pnPnzr9IDmbGsmXLmDFjBtOmTWPo0KGcf/75nHjiiTz00EOcccYZnkyc94mUZbt377YOHTrY4Ycfblu3bo06nCL35ZdfRh1CqTNt2jRr3ry5NW7c2P7617/u04DFXbt22ZAhQ6xhw4YGWKdOnWz8+PEpjNZFDR9sGEjXwYYffPABF154IYMHD+amm26KOpwil5OTQ/ny5aMOo1Qys0JfRezcuZMXXniBhx9+mFWrVtGlSxeuv/56zj33XKpXr17EkbooJTPY0JNIGZWTk0ObNm3Izs5m5syZ+c7JVJoNHDiQfv36RR1G2tq2bRvPPfccjz/+OKtXr6ZixYqceeaZXHTRRVxwwQXUqVMn6hDdfvKp4NPYyy+/zOzZs3nkkUfKZAJx0TvwwAPp168fy5cvZ/z48dxyyy3MmTOHG264gbp169KxY0eeeuopli9fHnWoLoU8iZRB27dv5y9/+QsnnHACPXr0iDocV8aVL1+ejh07MnDgQBYuXMi0adO477772LJlC3/84x9p2LAhnTt35vnnn2fdunVRh+uKmCeRMugf//gHy5cv57HHHivTd89069Yt6hBcHEm0adOG++67j6lTpzJv3jzuv/9+Vq1aRe/evTn88MM577zzePXVV/0W7TLC+0TKmPXr19O0aVNOPvlkPvzww6jDSamlS5fSsGHDqMNwSTAzpk6dyhtvvMGbb77J0qVLAWjQoAHt27ff88rIyOCggw6KNli3R+R9IpLOkTRPUpakO/NYX1PSB5KmSZolqVe4vLKkb2OWPxCzTS1Jn0laEL4fnMpzKG0GDBjApk2b+Otf/xp1KCn39ttvRx2CS5Ik2rVrx2OPPcaSJUv497//zZNPPknHjh2ZMWMGd999N126dOHggw/m+OOPZ+rUqVGH7JKUsiQiqTzwDHAu0Bq4UlLruGJ9gNlmdizQGRgoqSKwEzg9XN4WOEfSieE2dwJfmFlz4IvwuyP4y/zvf/8711xzjT++1pVY5cqV48QTT+S2227j9ddfZ8GCBaxfv55PP/2U/v37s2rVKn79618zaNAg0qGlpLRL5ZXICUCWmS0ys13Am0B8I7YB1RU03FcD1gPZ4TiXrWGZA8JX7k9TN+Dl8PPLQPfUnULp8fXXX3PFFVcA8OCDD0YcjXP75uCDD6ZLly7cc889TJs2jS5dunDrrbfSo0cP1q9fH3V4rgCpTCL1gaUx35eFy2INAloBK4AZQF8z2w3BlYykqcAa4DMz+ybcpo6ZrQQI3w/L6+CSbpQ0WdLktWvXFtEplSxmxscff8ypp57KKaecwrx583juuefSZnqTm2++OeoQXArkPrHxySef5MMPP6Rdu3Z89dVXUYfl8pHKJJLXbUHx16ZnA1OBegTNVoMk1QAwsxwzaws0AE6QdMy+HNzMhphZhpll1K5dex9DL9lycnJ46623OO644+jatSuLFy/m6aef5rvvvqNXr15Rh1dspk2bFnUILkUkcdttt/H1119ToUIFOnXqxCOPPMLu3bujDs3FSWUSWQbE3jrTgOCKI1YvYETYfJUFLAZaxhYws43AOOCccNFqSXUBwvc1RR55CTZr1ixatWq1Z1rvYcOGsXDhQvr27UvVqlWjDq9Y+V+nZV9GRgb//e9/ufTSS7nnnnto1qwZd9xxB5MnT/b+khIilUlkEtBcUpOws/wKYFRcme+BMwAk1QGOAhZJqi3poHB5FeBMYG64zSigZ/i5J/B+Cs+hRFmxYgXnnnsuW7du5Z///CezZs2iV69eVKxYMerQnEuZGjVq8Prrr/PWW2/RokULnnzySdq3b0/Tpk25/fbbmTRpkieUCKV0nIikrsDTQHlgmJk9LKk3gJkNllQPGA7UJWj+GmBmr0pqQ9BpXp4g0b1tZg+G+zwEeBtoRJCELjWzAnveysI4ka1bt3Lqqacyf/58JkyYQLt27aIOKXLDhg3jd7/7XdRhuGK2fv163n//fd555x0+++wzsrOzadSoEaeddhodO3akY8eONG/evEwPtC0uPgFjqLQnkezsbLp168aYMWMYNWoUXbt2jTok50qEDRs2MGrUKEaNGsX48eP54YcfAKhTp86ehNKpUyd+9atfUa6cT9CxrzyJhEpzEjEzbr75ZgYPHlxmp3QvrBEjRvjcYG4PM2PevHmMHz+eCRMmMGHCBL777jsguIX41FNPpXPnznTq1Ik2bdr4YwSSkEwS8ScblnBPPPEEgwcP5vbbb/cEEmfx4sVRh+BKEEm0bNmSli1bcuONNwLw/fffM378eMaNG8e4ceN4//2gC/Wggw6ic+fOPProo7Ro0SLKsEs9v74rwd555x1uv/12LrvssrSYxsS5otaoUSN+85vfMHToULKysli6dCmvvvoql1xyCePHj+fEE09k7NixUYdZqnlzVoSWLVvG4MGDqVSpEtWrV9/zqlatGps2baJXr14cf/zxfPHFF1SuXDnqcEucTz75hHPOOSdxQefysHjxYs4//3zmz5/Pc889x/XXXx91SCWON2eVcH369GHUqPi7nn/WvHlz3n//fU8g+fAE4vZHkyZN+Prrr7n88su54YYbmDdvHgMGDPC+kn3kzVkR+fzzzxk1ahQDBgxg586drFu3ju+++46ZM2fy73//m88++4xvv/2WQw89NOpQS6xBgwZFHYIr5WrWrMno0aPp06cPTzzxBD169GDr1q15lt2xYwfz588nOzu7mKMs2fxKJAI5OTn88Y9/pEmTJvTt25eKFStSq1YtatWqFXVopcrOnTujDsGVARUqVGDQoEG0bNmSvn370qFDB/72t7+xdOlSZs+ezaxZs5g9ezaLFi1i9+7dHH300Tz11FN06dIl6tBLBE8iEXjxxReZMWMG77zzjjdVOVdC3HLLLTRr1ozLLruMzp07A0GCadGiBe3atePqq6/m0EMP5amnnuKss87iggsu4Iknnkj7u7u8Y72Ybdq0iebNm9OyZUu+/PJLH1W7H2bMmMGvfvWrqMNwZczixYv573//S8uWLWnevDkHHHDAL9bv3LmTv//97zz00ENs376dW2+9lXvvvZeDDy57z8eL/MmGbm8PP/wwP/zwA0899ZQnkP1Ut27dqENwZVCTJk3o0aMHrVu33iuBAFSqVIk//elPLFiwgF69evH000/TvHlznnzyyT2P/U0nSSURSRmSbpP0uKQHJV0myRvw91FWVhZPP/001157Lccff3zU4ZR6L7/8cuJCzqVInTp1GDJkCFOmTKFNmzb069ePRo0a0a5dO+69916++eabtJi6vsA+EUnXAv9DMEV7JjAPqAx0AO6QNBO418y+T3GcZcLtt99OxYoVefjhh6MOxTlXRNq2bcsXX3zBnDlzGD16NKNHj+aRRx6hf//+HHbYYXTt2pX69euza9cudu3axc6dO/d8LleuHC1atODoo4+mdevWHHnkkVSoULq6qhNFWxU4xcy257VSUlugOcFsuq4AY8eOZeTIkTz88MPeDONcGSOJ1q1b07p1a26//XbWrVvHJ598wujRo3nvvffYtGkTlSpVolKlSlSsWHHP+65du3jllVf27KdSpUocddRRtG7dmjPOOIMrr7wy6ecEbdy4kR07dnD44Yen6jTz5B3rxSAnJ4fjjz+ejRs3MmfOHKpUqRJZLGXJunXrOOSQQ6IOw7kCmVmB/Z9bt25lzpw5v7idePr06SxdupSDDjqI6667jt///vc0bdp0r2137drFxx9/zCuvvMLo0aPZtWsXRx11FKeffjqnnXYanTt3Zn+e7Fpks/hKqg3cADQm5urFzErFwxyiTiJDhw7lhhtu4K233uKyyy6LLI6yZvr06bRp0ybqMJwrcmbGxIkTGTRoECNGjCAnJ4euXbtyyy23cNZZZzFp0iReeeUV3nrrLdatW8dhhx3GVVddRf369Rk3bhxffvnlnkGTbdq0oVOnTtSvX5+qVatSrVq1X7yOOeYYDjrooDzjKMok8jUwgaBfJCfmRN9NulYiFGUS2b17Nw0aNODII49kwoQJfkdWERo4cCD9+vWLOgznUmr58uUMGTKE559/ntWrV1O9enW2bNlC5cqV6d69O9dccw1dunT5RV/KTz/9RGZmJv/6178YO3YsEydOZMeOHXnu/5NPPuHss8/Oc11RJpGpZtY2YcESKsoksmDBAlq0aMGLL77oT+ErYp5EXDrZtWsX7777Lh9//DGdO3fmkksuoUaNGklta2bs2LGDrVu37nn9+OOPbN26lXbt2uXbLFyUEzCOltTVzD5KsrwL5SYvv6W36Plof5dOKlasyJVXXsmVV165z9tKokqVKlSpUmW/+kjykuxgw74EiWSHpC3ha3OijSSdI2mepCxJd+axvqakDyRNkzRLUq9weUNJYyXNCZf3jdnmfknLJU0NXyX6WbGZmZlUqlSJ1q1bRx1KmdOnT5+oQ3Au7SWVRMysupmVM7PK4efqZlbgdZSk8sAzwLlAa+BKSfG/SfsAs83sWKAzMFBSRSAb6GdmrYATgT5x2z5lZm3DV4m+OsrMzOTYY4/Nc+Sr2z8ff/xx1CE4l/aSnvZE0oWSnghf5yexyQlAlpktMrNdwJtAt7gyBlRX0NtcDVgPZJvZSjObAmBmW4A5QP1kYy0pdu/ezZQpU7wpK0Vmz54ddQjOpb1kpz0ZQNCkNTt89Q2XFaQ+EDuRzDL2TgSDgFbACmAG0NfMfjFPgKTGQDvgm5jFt0iaLmmYpDxnPZN0o6TJkiavXbs2QaipsXDhQjZv3uxJxDlXZiV7JdIV6GJmw8xsGHBOuKwged3LGn8r2NnAVKAe0BYYJGlPM5mkasC7wB/MLLcP5jmgaVh+JTAwr4Ob2RAzyzCzjKLuSEpWZmYm4J3qqZLX4CvnXPHal1l8D4r5XDOJ8suAhjHfGxBcccTqBYywQBbBHF0tASQdQJBAXjOzEbkbmNlqM8sJr1heIGg2K5FyO9WPPvroqEMpk7p37x51CM6lvWSTyF+B/0oaLullgkGHjyTYZhLQXFKTsLP8CiD+geLfA2cASKoDHAUsCvtIXgTmmNmTsRtIip146iJgZpLnUOwyMzNp06aNd6qnyIsvvhh1CM6lvaTGiZjZG5LGAe0JmqnuMLNVCbbJlnQLMAYoDwwzs1mSeofrBwMPAcMlzYjZ7w+SOgC/BWZImhru8u7wTqzHwokfDVgC3LQP51tszIwpU6YU6p5ul5yNGzdGHYJzaS/RVPAtzWyupOPCRcvC93qS6uXeQZWf8Jf+R3HLBsd8XgGclcd2E8m7TwUz+21BxywpFi5cyKZNm8jIKHCwp3POlWqJrkT+CNxI3p3XBpxe5BGVET5SPfU6duwYdQjOpb0Ck4iZ3Rh+PNfMfjF7lySfc6IA3qmeel63zkUv2Y71r5Nc5kLeqZ56gwcPTlzIOZdSifpEDicYIFhFUjt+7qeoARyY4thKLe9Ud86li0R9ImcD1xKM8Yi91XYLcHeKYir1cjvVvT/EOVfWJeoTeRl4WdLFpeUBVCWBj1QvHpdffnnUITiX9pIdJ/KupPOAo4HKMcsfTFVgpVlmZiYVK1b0jt8U2759e9QhOJf2kp2AcTBwOXArQb/IpcARKYyrVMvtVK9YsWLUoZRpo0bFT4DgnCtuyd6ddbKZXQNsMLMHgJP45bxYLmRmZGZmelOWcy4tJJtEctsNtkmqB/wENElNSKWbj1R3zqWTfXnG+kHA48AUgtHqQ1MVVGnmnerF57bbbos6BOfSXrKPx33IzDaGd2gdAbQ0s3tTG1rp5J3qxWf8+PFRh+Bc2ks02LBHAeuIfc6HC3inevHJzMykc+fOUYfhXFpL1Jx1Qfh+GHAy8K/w+2nAOMCTSIzckeo+fsE5ly4SDTbsBSBpNNDazFaG3+sCz6Q+vNJl0aJFbNy40ftDikndunUTF3LOpVSyd2c1zk0godVAixTEU6p5p3rxuuqqq6IOwbm0l2wSGSdpjKRrJfUEPgTGpjCuUim3U/2YY46JOpS08Prrr0cdgnNpL9lpT24JO9lznwI0xMxGpi6s0ikzM5Nf/epX3qleTFauXJm4kHMupZK9EsHMRpjZbeErqQQi6RxJ8yRlSbozj/U1JX0gaZqkWZJy+2AaShoraU64vG/MNrUkfSZpQfh+cLLnkEo+Ut05l44KTCKSJobvWyRtjnltkbQ5wbblCTrfzwVaA1dKah1XrA8w28yOBToDAyVVBLKBfmbWCjgR6BOz7Z3AF2bWHPgi/B4571Qvfl7XzkWvwCRiZh3C9+pmViPmVd3MaiTY9wlAlpktMrNdwJtAt/hDANUlCagGrAeyzWylmU0Jj70FmEPwcCzCfbwcfn4Z6J7MiaZabqe6T3dSfE499dSoQ3Au7SW6EqlV0CvBvusDS2O+L+PnRJBrENAKWAHMAPqa2e64GBoD7YBvwkV1cu8UC98Pyyf2GyVNljR57dq1CULdf5MnT/ZO9WL21FNPRR2Cc2kvUcd6JsHVgvJYZ8CRBWyb3zaxzgamAqcDTYHPJE0ws80AkqoB7wJ/yF2WLDMbAgwByMjIiD9ukfvqq6/IyMjwTnXnXFpJ1JzVxMyODN/jXwUlEAiuPGKni29AcMURqxcwwgJZwGKgJYCkAwgSyGtx06usDgc75g56XJPoJFNt+/btTJo0iQ4dOkQdinPOFauk786SdLCkEySdmvtKsMkkoLmkJmFn+RVA/FOEvgfOCPdfBzgKWBT2kbwIzDGzJ+O2GQX0DD/3BN5P9hxSZdKkSfz000+eRIrZhRdeGHUIzqW9pMaJSLoe6EtwNTGV4I6pfxM0Q+XJzLIl3QKMAcoDw8xslqTe4frBwEPAcEkzCJq/7jCzHyR1AH4LzJA0Ndzl3Wb2ETAAeFvSdQRJ6NJ9O+WiN3HiRABOOeWUiCNJL1WqVIk6BOfSXrLPE+kLtAf+Y2anSWoJPJBoo/CX/kdxywbHfF4BnJXHdhPJu08FM1tHePVSUkyYMIGjjz6aWrUS3WvgitJbb71Fv379og7DubSWbHPWDjPbASCpkpnNJWh6Sns5OTl8/fXXdOzYMXFh55wrY5K9ElkWPtnwPYI7qDawdyd5WpoxYwabN2/2/hDnXFpKdu6si8KP90saC9QEPklZVKVIbn+IJ5Hi17t376hDcC7tJdWcJelvkk4GMLMvzWxUOAo97U2cOJGGDRtyxBFHRB1K2pk1a1bUITiX9pLtE5kC/DmcSPFxST63B8GkixMmTPCrkIhMmDAh6hCcS3tJJREze9nMuhLMhzUfeFTSgpRGVgosWbKEFStWeBJxzqWtpAcbhpoRjChvDMwt8mhKmdz+EL8zKxoHHXRQ1CE4l/aS7RPJvfJ4EJgJHG9mF6Q0slJgwoQJ1KxZk6OPPjrqUNLSddddF3UIzqW9ZK9EFgMnmdk5ZvaSmW1MYUylxsSJEznllFMoV25fL+hcUXjvvfeiDsG5tJdoKvjGEIwyN7Mf8lgvSQ1SFFuJ9sMPPzBnzhxvyorQwoULow7BubSXaJzI45LKEUxymAmsBSoT9I2cRjD9yH0EM/amla+++grw8SHOufRWYBIxs0vDx9JeDfwOqAtsI3jS4EfAw7nToaSbiRMnUqlSJdq3bx91KGmrdev4py0754pbwhHrZjYbuKcYYilVJkyYQPv27alUqVLUoaStc889N+oQnEt73iNcCNu2bSMzM9ObsiL2zDPPRB2Cc2nPk0ghfPPNN2RnZ3unesR27EjLllTnShRPIoUwceJEJHHSSSdFHYpzzkUq0S2+Z0u6JI/lV0vqkrqwSraJEydyzDHHcPDBB0cdSlrr0iVtfwSdKzESXYk8AHyZx/IvCEavp53s7Gx/CFUJUb9+/ahDcC7tJUoiB5rZ2viFZrYKqJpo55LOkTQvnP33zjzW15T0gaRpkmZJ6hWzbpikNZJmxm1zv6TlkqaGr66J4ihK06dPZ+vWrd6pXgIMHz486hCcS3uJkkhlSXvdBizpAKBKQRtKKg88A5wLtAauDMecxOoDzDazY4HOwEBJFcN1w4Fz8tn9U2bWNnx9lE+ZlMidftyvRJxzLnESGQG8IGnPVUf4eXC4riAnAFlmtih8gNWbQLe4MgZUlySgGrAeyAYws/Hh9xJl4sSJHHHEETRokJazvTjn3C8kSiJ/BlYD30nKlDQFWEIw/cmfE2xbH1ga831ZuCzWIKAVwfPaZwB9zWx3EnHfIml62OSVZ++2pBslTZY0ee3avVrkCsXMmDhxojdllRA9e/aMOgTn0l6BScTMss3sTqAhcC3QE2hkZnea2U8J9q28dhn3/WxgKlAPaAsMklQjwX6fA5qG5VcCA/OJfYiZZZhZRu3atRPsMjlLly5l1apVnHzyyUWyP7d/Vq5cGXUIzqW9RLf49pDUg6BfoznBxIsZkqonse9lBMknVwOCK45YvYARFsgimHK+ZUE7NbPVZpYTXrG8QNBsViwWLAge5tiqVaviOqQrwKeffhp1CM6lvURzZ+X14KlaQBtJ15nZvwrYdhLQXFITYDlwBXBVXJnvCWYCniCpDnAUsKiggCTVNbPcP0EvInhIVrHIysoCoFmzZsV1SOecK9ESzeLbK6/lko4A3gZ+XcC22ZJuAcYA5YFhZjZLUu9w/WDgIWC4pBkEzV935D63RNIbBHdsHSppGXCfmb0IPCapLUHT2BLgpqTPdj9lZWVRqVIlH59QQvjkl85FT2bx3RRJbihNMbPjijielMjIyLDJkyfv934uuugi5s+fz6xZs4ogKuecK9kkZZpZRkFlCjV3lqSjgJ2FiqoUW7hwoTdllSCffPJJ1CE4l/YKbM6S9AF731FVi+DhVL9NVVAlkZmRlZXl8zWVILNmzeKcc/Ibj+qcKw6JOtafiPtuwDpgQTiAMG2sXLmS7du3+5WIc87FSNSxntfki0g6RdJVZtYnNWGVPLl3ZjVt2jTiSFyuJk2aRB2Cc2kv4eNxc4V3RF0FXEYwniPRtCdlit/eW/L06NEj6hCcS3uJBhu2kPQXSXMIpihZSnBH12lm9o9iibCEyMrKokKFCjRq1CjqUFxo2LBhUYfgXNpLdCUyF5gAXBCOKEfSbSmPqgTKysqiSZMmVKiQ9MWbS7ENGzZEHYJzaS/RLb4XA6uAsZJekHQGec+JVeZlZWV5U5ZzzsVJNAHjSDO7nGA+q3HAbUAdSc9JOqsY4isRcm/v9SRSspxyyilRh+Bc2ktqsKGZ/Whmr5nZ+QQTKU4F9npSYVm1du1atmzZ4kmkhDn22GOjDsG5tLfPI9bNbL2ZPW9mp6cioJLI78wqmZ599tmoQ3Au7RVq2pN040nEOefy5kkkCQsXLqRcuXI0btw46lCcc65E8SSShKysLI444ggqVqwYdSguxmWXXRZ1CM6lPU8iSfA7s0qmnTvTbiJp50ocTyJJyMrK8jmzSqD3338/6hCcS3ueRBJYv34969ev9ysR55zLgyeRBBYuXAj4nVnOOZeXlCYRSedImicpS9JegxMl1ZT0gaRpkmZJ6hWzbpikNZJmxm1TS9JnkhaE7wen8hz89t6S6w9/+EPUITiX9lKWRCSVB54BzgVaA1dKah1XrA8w28yOBToDAyXl3gI1HMjrsXV3Al+YWXPgC1I8cj43iRx55JGpPIwrhK+++irqEJxLe6m8EjkByDKzReFTEN8EusWVMaC6JAHVgPVANoCZjQ+/x+sGvBx+fhnoXvSh/ywrK4sGDRpQpUqVVB7GFcKkSZOiDsG5tJfKJFKf4PkjuZaFy2INAloBK4AZQF8z251gv3XMbCVA+H5YXoUk3ShpsqTJa9euLUz8gN/e65xzBUllEslryniL+342wWSO9YC2wCBJNYri4GY2xMwyzCyjdu3ahd6PJ5GS6/DDD486BOfSXiqTyDKgYcz3BgRXHLF6ASMskEXw2N2WCfa7WlJdgPB9TRHFu5fNmzezZs0aTyIl1NVXXx11CM6lvVQmkUlAc0lNws7yK4BRcWW+B84AkFQHOApYlGC/o4Ce4eeeQMpGnPntvSXbm2++GXUIzqW9lCURM8sGbgHGAHOAt81slqTeknqHxR4CTpY0g+BOqzvM7AcASW8A/waOkrRM0nXhNgOALpIWAF3C7ynhSaRkW758edQhOJf2UvrAcDP7CPgobtngmM8rgDyfkGhmV+azfB3h1Uuq5d7e61OeOOdc3nzEegGysrKoU6cO1apVizoUl4fjjjsu6hCcS3ueRArgd2aVbKeddlrUITiX9jyJFMCTSMk2cODAqENwLu15EsnHtm3bWL58uScR55wrgCeRfCxaFNxp7EnEOefy50kkHz57b8l3wQUXRB2Cc2nPk0g+/Pbekq9q1apRh+Bc2vMkko+srCwOOeQQDj44pY8rcfvBR6w7Fz1PIvnwO7Occy4xTyL58CTinHOJeRLJw86dO1m6dKknkRLupptuijoE59KeJ5E8LFmyhN27d3sSKeFmz54ddQjOpT1PInnwO7NKhwkTJkQdgnNpz5NIHnyMiHPOJceTSB6ysrKoUaMGhx56aNShuALUrFkz6hCcS3ueRPLw3Xff0bhxY6S8HhPvSorrr78+6hCcS3ueRPKwbt06vwopBd57772oQ3Au7XkSycP69es55JBDog7DJZD7+GLnXHRSmkQknSNpnqQsSXfmsb6mpA8kTZM0S1KvRNtKul/ScklTw1fXoo573bp11KpVq6h365xzZU7KnrEuqTzwDNAFWAZMkjTKzGJv7u8DzDazCyTVBuZJeg3ISbDtU2b2RCriNjO/EiklWrVqFXUIzqW9VF6JnABkmdkiM9sFvAl0iytjQHUFPdjVgPVAdpLbpsTmzZvJycnxJFIKdO1a5Behzrl9lMokUh9YGvN9Wbgs1iCgFbACmAH0NbPdSWx7i6TpkoZJKtJpdtetWwfgzVmlwLPPPht1CM6lvVQmkbzuj7W472cDU4F6QFtgkKQaCbZ9Dmgall8J5PmgbUk3SposafLatWuTDjo3ifiVSMm3ffv2qENwLu2lMoksAxrGfG9AcMURqxcwwgJZwGKgZUHbmtlqM8sJr1heIGj62ouZDTGzDDPLqF27dtJBr1+/HvAk4pxzyUhlEpkENJfURFJF4ApgVFyZ74EzACTVAY4CFhW0raS6MdtfBMwsyqC9Oav0OOOMM6IOwbm0l7K7s8wsW9ItwBigPDDMzGZJ6h2uHww8BAyXNIOgCesOM/sBIK9tw10/JqktQfPWEqBI5wP35qzSo1GjRlGH4FzaS1kSATCzj4CP4pYNjvm8Ajgr2W3D5b8t4jB/Ibc5yx+LW/K99NJL9OvXL+ownEtrPmI9zrp166hZsyYVKqQ0vzrnXJngSSTOunXrvCnLOeeS5Ekkjo9WLz2uueaaqENwLu15Eonj82aVHqtXr446BOfSnieRON6cVXqMGTMm6hCcS3ueROJ4EnHOueR5EomRnZ3Npk2bPImUEhUrVow6BOfSnieRGBs2bAB8tHppceutt0YdgnNpz5NIDB+tXrp4n4hz0fMkEsMnXyxdZs4s0mnTnHOF4Ekkhk++6Jxz+8aTSAxvzipdGjduHHUIzqU9TyIxvDmrdLn44oujDsG5tOdJJMa6desoX748NWrUiDoUl4SXXnop6hCcS3ueRGLkTnki5fV0XlfS5F45Ouei40kkhk++6Jxz+8aTSAyffLF0Ofnkk6MOwbm050kkhs+bVbq0bds26hCcS3ueRGJ4c1bp8uyzz0YdgnNpL6VJRNI5kuZJypJ0Zx7ra0r6QNI0SbMk9Uq0raRakj6TtCB8L7KHoXtzlnPO7ZuUJRFJ5YFngHOB1sCVklrHFesDzDazY4HOwEBJFRNseyfwhZk1B74Iv++3HTt2sG3bNr8Scc65fZDKK5ETgCwzW2Rmu4A3gW5xZQyoruCe2mrAeiA7wbbdgJfDzy8D3YsiWB9oWPpccsklUYfgXNqrkMJ91weWxnxfBvw6rswgYBSwAqgOXG5muyUVtG0dM1sJYGYrJR2W18El3QjcGH7dKSmp2fp69+5N7969kylaGh0K/BB1EBHzOgh4PQS8HgqugyMSbZzKJJLXiD2L+342MBU4HWgKfCZpQpLbFsjMhgBDACRNNrOMfdm+LPJ68DrI5fUQ8HrY/zpIZXPWMqBhzPcGBFccsXoBIyyQBSwGWibYdrWkugDh+5oUxO6ccy4JqUwik4DmkppIqghcQdB0Fet74AwASXWAo4BFCbYdBfQMP/cE3k/hOTjnnCtAypqzzCxb0i3AGKA8MMzMZknqHa4fDDwEDJc0g6AJ6w4z+wEgr23DXQ8A3pZ0HUESujSJcIYU4amVZl4PXge5vB4CXg/7WQcy26euBuecc24PH7HunHOu0DyJOOecK7QynUQSTbtSlkgaJmlN7HiYgqaIkXRXWC/zJJ0dTdRFS1JDSWMlzQmn0ekbLk+3eqgs6duY6YQeCJenVT1AMHOGpP9KGh1+T8c6WCJphqSpkiaHy4quHsysTL4IOuQXAkcCFYFpQOuo40rh+Z4KHAfMjFn2GHBn+PlO4NHwc+uwPioBTcJ6Kh/1ORRBHdQFjgs/Vwfmh+eabvUgoFr4+QDgG+DEdKuH8Nz+CLwOjA6/p2MdLAEOjVtWZPVQlq9Ekpl2pcwws/EE08bEym+KmG7Am2a208wWA1kE9VWqmdlKM5sSft4CzCGYOSHd6sHMbGv49YDwZaRZPUhqAJwHDI1ZnFZ1UIAiq4eynETymjqlfkSxROUXU8QAuVPElPm6kdQYaEfwV3ja1UPYjDOVYDDuZ2aWjvXwNHA7sDtmWbrVAQR/QHwqKTOcDgqKsB5SOe1J1PZ76pQyrEzXjaRqwLvAH8xsczC/Z95F81hWJurBzHKAtpIOAkZKOqaA4mWuHiSdD6wxs0xJnZPZJI9lpboOYpxiZivCeQY/kzS3gLL7XA9l+UokmWlXyrr8pogps3Uj6QCCBPKamY0IF6ddPeQys43AOOAc0qseTgEulLSEoCn7dEmvkl51AICZrQjf1wAjCZqniqweynISSWbalbIuvyliRgFXSKokqQnQHPg2gviKlIJLjheBOWb2ZMyqdKuH2uEVCJKqAGcCc0mjejCzu8ysgZk1Jvi//y8z+w1pVAcAkqpKqp77GTgLmElR1kPUdw6k+K6ErgR36CwE7ok6nhSf6xvASuAngr8mrgMOIXhw14LwvVZM+XvCepkHnBt1/EVUBx0ILr2nE8wOPTX8GUi3emgD/Desh5nAX8LlaVUPMefWmZ/vzkqrOiC4O3Va+JqV+3uwKOvBpz1xzjlXaGW5Ocs551yKeRJxzjlXaJ5EnHPOFZonEeecc4XmScQ551yheRJxZZ4kkzQw5vv/Srq/iPY9XNIlRbGvBMe5NJydeGzc8saSrkr18Z3LjycRlw52Aj0kHRp1ILEkld+H4tcBN5vZaXHLGwN5JhFJZXlaI1dCeBJx6SCb4DnSt8WviL+SkLQ1fO8s6UtJb0uaL2mApKvD53TMkNQ0ZjdnSpoQljs/3L68pMclTZI0XdJNMfsdK+l1YEYe8VwZ7n+mpEfDZX8hGEg5WNLjcZsMADqGz4q4TdK1kt6R9AHBpHtVFTxrZlL4XI1uCeKrK2l8uL+ZkjoWss5dmvC/VFy6eAaYLumxfdjmWKAVwRT7i4ChZnaCgodd3Qr8ISzXGOgENAXGSmoGXANsMrP2kioBX0n6NCx/AnCMBVNt7yGpHvAocDywgSAJdDezByWdDvyvmU2Oi/HOcHlu8roWOAloY2brJT1CMOXH78KpUL6V9DlwdT7x9QDGmNnD4ZXSgftQXy4NeRJxacGC2XxfAf4H2J7kZpMsnC5b0kIgNwnMAGKbld42s93AAkmLgJYEcxS1ibnKqUkwD9Eu4Nv4BBJqD4wzs7XhMV8jeNjYe0nGm+szM8t9tsxZBBMR/m/4vTLQqID4JgHDwoks3zOzqft4bJdmPIm4dPI0MAV4KWZZNmGzbjiBY8WYdTtjPu+O+b6bX/7fiZ87yAim1L7VzMbErginJf8xn/jynbN+H8XuX8DFZjYvLo484wvXnUrwMKf/k/S4mb1SRHG5Msj7RFzaCP86f5ugkzrXEoLmIwie6nZAIXZ9qaRyYT/JkQQT140Bfh/+RY+kFuEsqgX5Bugk6dCwKelK4MsE22wheBRwfsYAt4ZJA0ntYpbvFZ+kIwiew/ECwYzIxyU4vktzfiXi0s1A4JaY7y8A70v6lmA20/yuEgoyj+CXfR2gt5ntkDSUoK9kSvgLfC0/P4I0T2a2UtJdwFiCK4iPzOz9grYhmKk3W9I0YDhBX0qshwiuwKaHcSwBzid4ZGxe8XUG/iTpJ2ArQd+Oc/nyWXydc84VmjdnOeecKzRPIs455wrNk4hzzrlC8yTinHOu0DyJOOecKzRPIs455wrNk4hzzrlC+39ExmZ2pke1lQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.plot(tree, aucs_val, color='black', linestyle='solid')\n",
    "\n",
    "\n",
    "plt.ylim(0.80, 0.84)\n",
    "plt.xlim(0, 510)\n",
    "plt.xticks(range(0, 501, 100))\n",
    "plt.yticks(np.linspace(0.80, 0.84, 9))\n",
    "\n",
    "\n",
    "plt.vlines(180, 0, 1, color='grey', linestyle='dashed', linewidth=0.9)\n",
    "\n",
    "plt.title('Selecting the number of trees')\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('AUC (validation)')\n",
    "\n",
    "# plt.savefig('ch06-figures/06_xgb_number_trees.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.77424\tval-auc:0.73571\n",
      "[10]\ttrain-auc:0.84733\tval-auc:0.78964\n",
      "[20]\ttrain-auc:0.86359\tval-auc:0.80617\n",
      "[30]\ttrain-auc:0.87855\tval-auc:0.81478\n",
      "[40]\ttrain-auc:0.88701\tval-auc:0.81990\n",
      "[50]\ttrain-auc:0.89458\tval-auc:0.82595\n",
      "[60]\ttrain-auc:0.90159\tval-auc:0.82807\n",
      "[70]\ttrain-auc:0.90650\tval-auc:0.82801\n",
      "[80]\ttrain-auc:0.91110\tval-auc:0.82940\n",
      "[90]\ttrain-auc:0.91428\tval-auc:0.82997\n",
      "[100]\ttrain-auc:0.91801\tval-auc:0.83221\n",
      "[110]\ttrain-auc:0.92205\tval-auc:0.83290\n",
      "[120]\ttrain-auc:0.92460\tval-auc:0.83285\n",
      "[130]\ttrain-auc:0.92843\tval-auc:0.83414\n",
      "[140]\ttrain-auc:0.93100\tval-auc:0.83463\n",
      "[150]\ttrain-auc:0.93296\tval-auc:0.83525\n",
      "[160]\ttrain-auc:0.93551\tval-auc:0.83554\n",
      "[170]\ttrain-auc:0.93788\tval-auc:0.83638\n",
      "[179]\ttrain-auc:0.93962\tval-auc:0.83648\n"
     ]
    }
   ],
   "source": [
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain,\n",
    "                  num_boost_round=180, verbose_eval=10,\n",
    "                  evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb = model.predict(dval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8364807100821002"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_val, y_pred_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7854738320699426\n",
      "0.8253826499721489\n",
      "0.8364807100821002\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_val, y_pred_dt))\n",
    "print(roc_auc_score(y_val, y_pred_rf))\n",
    "print(roc_auc_score(y_val, y_pred_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_val, y_pred_xgb)\n",
    "plt.plot(fpr, tpr, color='black')\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_val, y_pred_rf)\n",
    "plt.plot(fpr, tpr, color='grey', linestyle='dashed', alpha=0.9)\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_val, y_pred_dt)\n",
    "plt.plot(fpr, tpr, color='grey', linestyle='dashed', alpha=0.9)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='black', lw=0.7, linestyle='dashed', alpha=0.5)\n",
    "\n",
    "plt.xlim([-0.02, 1.02])\n",
    "plt.ylim([-0.02, 1.02])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.title('ROC curve')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_full = (df_train_full.status == 'default').values\n",
    "y_test = (df_test.status == 'default').values\n",
    "\n",
    "del df_train_full['status']\n",
    "del df_test['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_train_full = df_train_full.fillna(0).to_dict(orient='records')\n",
    "dict_test = df_test.fillna(0).to_dict(orient='records')\n",
    "\n",
    "dv = DictVectorizer(sparse=False)\n",
    "X_train_full = dv.fit_transform(dict_train_full)\n",
    "X_test = dv.transform(dict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_final = RandomForestClassifier(n_estimators=200, max_depth=10, min_samples_leaf=5, random_state=1)\n",
    "rf_final.fit(X_train_full, y_train_full)\n",
    "\n",
    "y_pred_rf = rf.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain_full = xgb.DMatrix(X_train_full, label=y_train_full, feature_names=dv.feature_names_)\n",
    "dtest = xgb.DMatrix(X_test, label=y_test, feature_names=dv.feature_names_)\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.1,\n",
    "    'max_depth': 3,\n",
    "    'min_child_weight': 1,\n",
    "\n",
    "    'objective': 'binary:logistic',\n",
    "    'eval_metric': 'auc',\n",
    "    'nthread': 8,\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "num_trees = 160\n",
    "\n",
    "xgb_final = xgb.train(xgb_params, dtrain_full, num_boost_round=num_trees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xgb = xgb_final.predict(dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8241777039245393\n",
      "0.8322845901959826\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_test, y_pred_rf))\n",
    "print(roc_auc_score(y_test, y_pred_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01387723, 0.04793733, 0.1226385 , 0.00833288, 0.0250906 ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.18724799, 0.00699145, 0.        ,\n",
       "       0.        , 0.17292753, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.0302163 ,\n",
       "       0.        , 0.24421676, 0.12152239, 0.01900104])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('age', 0.013877231876069153),\n",
       " ('amount', 0.04793733336664824),\n",
       " ('assets', 0.12263850423922844),\n",
       " ('debt', 0.008332876385504902),\n",
       " ('expenses', 0.025090602575881322),\n",
       " ('home=ignore', 0.0),\n",
       " ('home=other', 0.0),\n",
       " ('home=owner', 0.0),\n",
       " ('home=parents', 0.0),\n",
       " ('home=private', 0.0),\n",
       " ('home=rent', 0.0),\n",
       " ('home=unk', 0.0),\n",
       " ('income', 0.18724798934476602),\n",
       " ('job=fixed', 0.006991445659436667),\n",
       " ('job=freelance', 0.0),\n",
       " ('job=others', 0.0),\n",
       " ('job=partime', 0.17292752964348085),\n",
       " ('job=unk', 0.0),\n",
       " ('marital=divorced', 0.0),\n",
       " ('marital=married', 0.0),\n",
       " ('marital=separated', 0.0),\n",
       " ('marital=single', 0.0),\n",
       " ('marital=unk', 0.0),\n",
       " ('marital=widow', 0.0),\n",
       " ('price', 0.030216298419676543),\n",
       " ('records=no', 0.0),\n",
       " ('records=yes', 0.24421675762093445),\n",
       " ('seniority', 0.12152238957522045),\n",
       " ('time', 0.019001041293152915)]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(dv.feature_names_, dt.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>feature</th>\n",
       "      <th>gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>records=yes</td>\n",
       "      <td>0.244217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>income</td>\n",
       "      <td>0.187248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>job=partime</td>\n",
       "      <td>0.172928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>assets</td>\n",
       "      <td>0.122639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>seniority</td>\n",
       "      <td>0.121522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amount</td>\n",
       "      <td>0.047937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>price</td>\n",
       "      <td>0.030216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>expenses</td>\n",
       "      <td>0.025091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>time</td>\n",
       "      <td>0.019001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.013877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>debt</td>\n",
       "      <td>0.008333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>job=fixed</td>\n",
       "      <td>0.006991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>home=rent</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>marital=separated</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>home=ignore</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>records=no</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>home=other</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>marital=widow</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>marital=unk</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>marital=single</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>marital=married</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>home=unk</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>marital=divorced</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>job=unk</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home=owner</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>job=others</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>home=parents</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>home=private</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>job=freelance</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              feature      gain\n",
       "26        records=yes  0.244217\n",
       "12             income  0.187248\n",
       "16        job=partime  0.172928\n",
       "2              assets  0.122639\n",
       "27          seniority  0.121522\n",
       "1              amount  0.047937\n",
       "24              price  0.030216\n",
       "4            expenses  0.025091\n",
       "28               time  0.019001\n",
       "0                 age  0.013877\n",
       "3                debt  0.008333\n",
       "13          job=fixed  0.006991\n",
       "10          home=rent  0.000000\n",
       "20  marital=separated  0.000000\n",
       "5         home=ignore  0.000000\n",
       "25         records=no  0.000000\n",
       "6          home=other  0.000000\n",
       "23      marital=widow  0.000000\n",
       "22        marital=unk  0.000000\n",
       "21     marital=single  0.000000\n",
       "19    marital=married  0.000000\n",
       "11           home=unk  0.000000\n",
       "18   marital=divorced  0.000000\n",
       "17            job=unk  0.000000\n",
       "7          home=owner  0.000000\n",
       "15         job=others  0.000000\n",
       "8        home=parents  0.000000\n",
       "9        home=private  0.000000\n",
       "14      job=freelance  0.000000"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = list(zip(dv.feature_names_, dt.feature_importances_))\n",
    "\n",
    "df_importance = pd.DataFrame(importances, columns=['feature', 'gain'])\n",
    "df_importance = df_importance.sort_values(by='gain', ascending=False)\n",
    "df_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_importance = df_importance[df_importance.gain > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = len(df_importance)\n",
    "plt.barh(range(num), df_importance.gain[::-1])\n",
    "plt.yticks(range(num), df_importance.feature[::-1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.81509463e-02, 8.42444361e-02, 7.75901403e-02, 1.33739427e-02,\n",
       "       3.62995916e-02, 1.26998064e-06, 5.25854792e-03, 2.57383446e-02,\n",
       "       8.51497558e-03, 3.24437831e-03, 1.44619658e-02, 0.00000000e+00,\n",
       "       1.50682384e-01, 4.78310889e-02, 9.96408243e-03, 2.85512508e-03,\n",
       "       5.65260797e-02, 0.00000000e+00, 2.43128646e-04, 6.42996557e-03,\n",
       "       1.68784119e-03, 6.67458246e-03, 0.00000000e+00, 1.97498337e-04,\n",
       "       6.62193254e-02, 8.41683921e-02, 8.30420483e-02, 1.25453198e-01,\n",
       "       3.11467205e-02])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "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>feature</th>\n",
       "      <th>gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>income</td>\n",
       "      <td>0.150682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>seniority</td>\n",
       "      <td>0.125453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amount</td>\n",
       "      <td>0.084244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>records=no</td>\n",
       "      <td>0.084168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>records=yes</td>\n",
       "      <td>0.083042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>assets</td>\n",
       "      <td>0.077590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>price</td>\n",
       "      <td>0.066219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.058151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>job=partime</td>\n",
       "      <td>0.056526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>job=fixed</td>\n",
       "      <td>0.047831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>expenses</td>\n",
       "      <td>0.036300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>time</td>\n",
       "      <td>0.031147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>home=owner</td>\n",
       "      <td>0.025738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>home=rent</td>\n",
       "      <td>0.014462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>debt</td>\n",
       "      <td>0.013374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>job=freelance</td>\n",
       "      <td>0.009964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>home=parents</td>\n",
       "      <td>0.008515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>marital=single</td>\n",
       "      <td>0.006675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>marital=married</td>\n",
       "      <td>0.006430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>home=other</td>\n",
       "      <td>0.005259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>home=private</td>\n",
       "      <td>0.003244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>job=others</td>\n",
       "      <td>0.002855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>marital=separated</td>\n",
       "      <td>0.001688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>marital=divorced</td>\n",
       "      <td>0.000243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>marital=widow</td>\n",
       "      <td>0.000197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>home=ignore</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>marital=unk</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>job=unk</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>home=unk</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              feature      gain\n",
       "12             income  0.150682\n",
       "27          seniority  0.125453\n",
       "1              amount  0.084244\n",
       "25         records=no  0.084168\n",
       "26        records=yes  0.083042\n",
       "2              assets  0.077590\n",
       "24              price  0.066219\n",
       "0                 age  0.058151\n",
       "16        job=partime  0.056526\n",
       "13          job=fixed  0.047831\n",
       "4            expenses  0.036300\n",
       "28               time  0.031147\n",
       "7          home=owner  0.025738\n",
       "10          home=rent  0.014462\n",
       "3                debt  0.013374\n",
       "14      job=freelance  0.009964\n",
       "8        home=parents  0.008515\n",
       "21     marital=single  0.006675\n",
       "19    marital=married  0.006430\n",
       "6          home=other  0.005259\n",
       "9        home=private  0.003244\n",
       "15         job=others  0.002855\n",
       "20  marital=separated  0.001688\n",
       "18   marital=divorced  0.000243\n",
       "23      marital=widow  0.000197\n",
       "5         home=ignore  0.000001\n",
       "22        marital=unk  0.000000\n",
       "17            job=unk  0.000000\n",
       "11           home=unk  0.000000"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = list(zip(dv.feature_names_, rf.feature_importances_))\n",
    "\n",
    "df_importance = pd.DataFrame(importances, columns=['feature', 'gain'])\n",
    "df_importance = df_importance.sort_values(by='gain', ascending=False)\n",
    "df_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_importance = df_importance[df_importance.gain > 0.01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = len(df_importance)\n",
    "plt.barh(range(num), df_importance.gain[::-1])\n",
    "plt.yticks(range(num), df_importance.feature[::-1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('records=no', 20.205886174933337),\n",
       " ('job=partime', 19.571840249333327),\n",
       " ('job=fixed', 14.703467482368419),\n",
       " ('home=owner', 12.168287986666668),\n",
       " ('seniority', 10.93204644173191),\n",
       " ('income', 8.676314024586466),\n",
       " ('assets', 5.918307806556342),\n",
       " ('amount', 5.400780562827161),\n",
       " ('time', 5.0175909627586215),\n",
       " ('home=parents', 4.499092019090908),\n",
       " ('home=rent', 4.028717651875),\n",
       " ('price', 3.7946846306606075),\n",
       " ('expenses', 3.589669993872826),\n",
       " ('age', 3.318183602467891),\n",
       " ('debt', 2.9418528487777773),\n",
       " ('marital=separated', 2.641880279076923),\n",
       " ('home=private', 2.4620430769999997),\n",
       " ('marital=married', 2.427789335636364),\n",
       " ('home=other', 2.3016982833333333),\n",
       " ('job=freelance', 2.25929752),\n",
       " ('job=others', 1.61876595),\n",
       " ('home=ignore', 0.7887506362000001)]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = model.get_score(importance_type='gain')\n",
    "scores = sorted(scores.items(), key=lambda x: x[1])\n",
    "list(reversed(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('price', 165),\n",
       " ('amount', 162),\n",
       " ('assets', 142),\n",
       " ('income', 133),\n",
       " ('seniority', 119),\n",
       " ('age', 109),\n",
       " ('expenses', 92),\n",
       " ('debt', 63),\n",
       " ('records=no', 60),\n",
       " ('time', 29),\n",
       " ('home=parents', 22),\n",
       " ('job=partime', 21),\n",
       " ('job=fixed', 19),\n",
       " ('marital=separated', 13),\n",
       " ('home=private', 12),\n",
       " ('marital=married', 11),\n",
       " ('home=rent', 8),\n",
       " ('home=owner', 6),\n",
       " ('home=ignore', 5),\n",
       " ('job=freelance', 4),\n",
       " ('home=other', 3),\n",
       " ('job=others', 1)]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = model.get_score(importance_type='weight')\n",
    "scores = sorted(scores.items(), key=lambda x: x[1])\n",
    "list(reversed(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [n for (n, s) in scores]\n",
    "scores = [s for (n, s) in scores]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7bEPWGOcAl0i6CLg9IuYUeyNOqDczq4ze3ggFXBsRX3vLSul/kDWizYEtgVfTprbJ75HOcXdEfKLENTakPzeSPs+IuFDSHcARwHxJ49N5fhARP3tbkdKItO8PJM2IiPPb7uOEejOzyujVU6PAPcBxknYCkLSdpN3JGso3geuBiwr2Hy1pj3RvcAJwP1mw7kGS3pPOsZWkPdu7qKQhEdESEReRTaXuDdwFnFJwf3EXSTtJ2hlYHxHXAZcAB/TYuzczsw716hFhRKyS9A1gRmpurwO3Am9ExK8kNQHzJB0GbCKb8ryQ7B7hbODmiNgkaRJwg6S+6dTfAB5r59JflHQo2ShxFfD7iNiQ7lk+kKZYXwFOAt4DTJa0KdX3uR78CMzMrAOK8CwbgKRxwNkRcWSNS+lQ30FDY9DEy2tdhlmX+btGrdokLY6IkcW29fapUTMzs3b16qnRzoiIWcCsGpdhZmZV5kbYgJxQb2bWczw1amZmueZGaGZmueZGaGZmueZ7hA2o5am1DD73jlqXYRXkXy8wqx6PCM3MLNfcCM3MLNfcCHuQpG9LOrud7ddIOq7I+mZJR1S2OjMzK8aNsD40k6VPmJlZlbkRdpOkr0t6VNIfyEJ/kTRE0p2SFkua05pcn4xP6x6TdKSkLcjyESekRHsn1JuZVZGfGu2GlCN4ArA/2We5hCygdwpwekQ8Lun9wBXAYemwwcAHgSHATLL0ifOAkRHx+aq+ATMzcyPspoPJoprWA0i6jSzodwxwY4pbAuhbcMxvImIT8Lik1WRZhR1yQr2ZWWW4EXZf2xyrzYCXIqK5zP3LysFyQr2ZWWX4HmH3zAY+KqmfpP7AvwHrgTWSjgdQZr+CY46XtJmkIcC7gUeBdUD/KtduZma4EXZLRCwBpgFLgd8Cc9KmE4FTJS0DVgJHFxz2KHAf8Huy+4j/ILtXuI8fljEzqz5PjXZTRHwP+F6RTR8usu+kEud4ARjVs5WZmVk5PCI0M7NccyM0M7Nc89RoA3JCvZlZz/GI0MzMcs2N0MzMcs2N0MzMcs33CBuQE+p7F6fRm9WWR4RmZpZrboRmZpZrboRlkvRKrWswM7Oe1/CNMH2pdY++D0m+d2pmlhMN2QglDZb0sKQryMJwvylpoaTlkr5TsN/Jad0ySb9M63aXdE9af4+k3dL6ayRdKmkmcJGkPSQ9kM773YJzDpI0O31B9gpJB5dZ8zWSfihpnqTVko5L6yVpcjpXi79028ysuhp55LMX8GngFuA4YDQg4DZJhwB/A74OHBQRz0vaLh33Y2BqRFwr6RTgh8AxaduewPiI2JhCdn8aEVMlnVFw3U8Cd0XE9yQ1AVsBSJqWamrr0oiYmpYHAWPJwnhvA6YDHwOagf2AHYCFkmZHxDNd/2jMzKxcjdwIn4yI+ZIuAQ4HHkrrtwGGkjWW6RHxPPwz4QHgQLLmA/BL4OKCc94YERvT8kHAsQX7XZSWFwJXS9ocuCUilqbzlzOSuyWl06+S9M60bixwQ7ruXyXdR5ZEcVvhgU6oNzOrjIacGk1eTX8K+EFENKef90TEf6X15SS5F+7zajvbshURs4FDgKeAX0o6GbIRYZoubftzcsHhGwqW1ebP9ouMmBIRIyNiZNNWA8o5xMzMytDIjbDVXcApkrYBkLSLpJ2Ae4CPS9o+rW+dGp0HnJCWTwTuL3HeuW32I51nd+DZiPg58F/AAZCNCAuaceHP1Led+a1mAxMkNUnakazJLij73ZuZWbc08tQoABExQ9J7gQckAbwCnBQRKyV9D7hP0kayqdNJwFlkU5vnAM+R3Wcs5gvAryR9gSx9vtU44BxJr6drnVzk2M64mWy6dhnZCPQrEfGXbp7TzMzKpIhyZg+tnvQdNDQGTby81mVYD/FXrJlVnqTFETGy2LbeMDVqZmbWZW6EZmaWaw1/jzCPnFBvZtZzPCI0M7NccyM0M7NccyM0M7Nc8z3CBtTbE+r96wRmVk0eEZqZWa65EZqZWa65EXaDpIGS/j0t7yxpeq1rMjOzznEj7J6BwL8DRMTTEXFcbcsxM7PO8sMy3XMhMETSUuBx4L0RMUzSJLKw3yZgGPC/gS2AT5FFMR0RES9IGgL8BNgRWA98JiIeqfabMDPLM48Iu+dc4E8R0Qyc02bbMLI0+9HA94D1EbE/8ABvJlZMAc6MiBHA2cAV1SjazMze5BFh5cyMiHXAOklrgd+l9S3A8JSfOAa4McVHAfQtdTIn1JuZVYYbYeUUptFvKni9iexz3wx4KY0mOxQRU8hGkPQdNNTZWWZmPcRTo92zDujflQMj4mVgjaTjAZTZryeLMzOzjrkRdkNE/A2YK2kFMLkLpzgROFXSMmAlcHRP1mdmZh3z1Gg3RcQni6y7Brim4PXgYtsiYg3w4cpWaGZm7fGI0MzMcs2N0MzMcs1Tow3ICfVmZj3HI0IzM8s1N0IzM8s1N0IzM8s13yNsQI2SUO+keTNrBB4RmplZrrkRmplZrrkRmplZrlW9EUoanL6bM7ckfVHSVrWuw8zMPCLsFklNXTz0i4AboZlZHahVI2yS9HNJKyXNkNRPUrOk+ZKWS7pZ0jsAJM2SdJmk2ZIeljRK0k2SHpd0QesJJZ0kaYGkpZJ+Vm6TknSNpCslzZH0mKQj0/rBad2S9DMmrR8naaakXwEtkpokTZa0MNX+2YL9ZkmaLukRSdenqKWzgJ2Bmek8TamGFZJaJH2phz9rMzNrR61+fWIo8ImI+Iyk3wDHAl8BzoyI+ySdD3yLbOQE8FpEHCLpC8CtwAjgBeBPki4DdgImAAdFxOuSriCLOJoqaRqwV5EaLo2IqWl5MPBBYAhZg3oP8CzwPyPiH5KGAjcAI9P+o4FhEbEmJcevjYhRkvqSxTLNSPvtD7wPeBqYm+r7oaQvA4dGxPOSRgC7RMQwAEkDi31gTqg3M6uMWjXCNRGxNC0vJmtAAyPivrTuWuDGgv1vS3+2ACsj4hkASauBXYGxZM1xoSSAfmSNjIiYUEY9v4mITcDj6Zx7A2uAH0tqBjYCexbsvyBFKAEcDgyXdFx6PYCs0b+W9vtzqnUpWcO9v821VwPvlvQj4A5gBkU4od7MrDJq1Qg3FCxvBAaWuf+mNsduInsPAq6NiK+1PbDMEWHbxhLAl4C/AvuRTSH/o2D7q4WXIBvJ3tXmuuN4+/t82+cdES+mZPp/Ac4APg6cUqReMzOrgHr5Zpm1wIuSDo6IOcCngPs6OKbQPcCtki6LiGclbQf0j4gnyxwRHi/pWmAP4N3Ao2Qjuz9HxCZJE4FS9xzvAj4n6d40Lbsn8FQH11sH9Aeel7QD2dTvbyX9iYJAXzMzq7x6aYQAE4Er068VrAY+Xe6BEbFK0jeAGZI2A14nG109WeYpHiVrvO8ETk/3Ba8AfivpeGAmbx0FFrqKbMpzibJ52eeAYzq43hTg95KeIbsP+otUN8DbRrVmZlY5isj37SZJ1wC3R8T0WtdSrr6DhsagiZfXuowO+btGzaxeSFocESOLbfPvEZqZWa7V09RoTUTEpFrXYGZmtZP7RtiI9t1lAIs87Whm1iM8NWpmZrnmRmhmZrnmqdEGVO8J9X5a1MwaiUeEZmaWa26EZmaWa26EZmaWaw3RCCXNa2fbOEm3V7OegmsPlPTvBa93ltQw31BjZmYN0ggjYkyta2grBf8OBP7ZCCPi6Yg4ruRBZmZWdxqiEUp6JaW7Ty5Ici9Mldg2pdqvSmnzZb2v7qbTAxcCQyQtTbUNlrQi7TtJ0i2SfidpjaTPS/qypIckzU8JGUgaIulOSYvTNffuyc/OzMza10i/PvExoJksH3AHshDe2WnbaGAfsrSJO9O+06uQTj84LTdD1kDbXGcYWUr9lsAfga9GxP6SLgNOBi4nS6I4PSIel/R+4ArgsLYFO6HezKwyGqkRjgVuiIiNwF8l3QeMAl4mS4JfDSDphrTv9Cqk03dkZkSsA9ZJWgv8Lq1vIUu13wYYA9yYJTgB0LfYiZxQb2ZWGY3UCNXOtmIJ89VIp+9IYUL9poLXm8g++82Al1pHlGZmVn0NcY8wmQ1MkNQkaUfgEGBB2jZa0h7p3uAE4H6AiJgQEc1FfqYWnPd4SZtJGsJb0+mfSSPFT1E6nb41ab5LIuJlYE0K/yXdB92vq+czM7POa5RGGMDNwHJgGXAv8JWI+Eva/gDZgysryKY1b+7EuVvT6X9PSqcnu083UdJ8smnRoqPAiPgbMDc9wDO50+8qcyJwqqRlwErg6C6ex8zMuqDuE+olbQ8siYjdK3Dua2iwdHqo/4R6f9eomdWbhk2ol7Qz2WjvklrXYmZmvVNdPywTEU/z1ic2e/r8kyp1bjMzawx13QitOCfUm5n1nLqeGjUzM6s0N0IzM8s1T402oHpLqPdTombWyDwiNDOzXHMjNDOzXHMjNDOzXOvVjbBSyfaSdpT0YMoWPFjS/5U0sMuFvnneWZKKfvOBmZlVRq9+WKaCyfYfAh6JiInp9ZwKXcfMzCqst48IezzZPmUUXgwckZLp+0l6QtIOkkZJWi5pS0lbS1opaVhavlrSwjSKPDqdq5+kX6djpgH9KvJBmJlZSb16RJj0eLK9pPOAkRHxeYDWUN2IWCjpNuACsqZ2XUSskPR94N6IOCVNoS6Q9Afgs8D6iBguaTiwpOffvpmZtScPjbBSyfalnA8sJAvzPSutOxw4StLZ6fWWwG5kmYo/BIiI5ZKWlzqppNOA0wCatt2xG+WZmVmhPDTCSiXbl7IdsA2wOVnDezXVcGxEPPqWwrKRZFk5WBExBZgCWQxTOceYmVnHevU9wqRSyfalTAG+CVwPXJTW3QWcqdT5JO1fUNuJad0wYHi3362ZmXVKbx8RtibbH0iWbB+kZHtJe/Nmsv2+ZE2pM8n2byPpZOCNiPiVpCZgnqTDgO8ClwPLUzN8AjgS+CnwizQlupQ3G7SZmVVJ3SfUd1Ulk+1rrd4S6v1do2ZW7xo2ob6rnGxvZmbl6pVTo5VOtjczs96jVzbC3s4J9WZmPadXTo2amZmVy43QzMxyzVOjDagaCfV+EtTM8sIjQjMzyzU3QjMzyzU3QjMzy7WqNkJJO0uanpabJR1RxjFdTpKvFUkDJf17F477dkFChZmZVUHVGqGkPhHxdEQcl1Y1Ax02wnolqb0HjQYCnW6EZmZWfeUksg+W9Iikq1LK+/WSxkuaK+lxSaPTz7yUvj5P0l7p2EmSbpT0O2BGOtcKSVuQ5fZNSCnvE0qdozNSEvwdkpal60xI60dIuk/SYkl3SRqU1s+SdHm63gpJo9P6ct/PNpLukbREUktr8jzZF3kPSe9tcjr2nJRQv1zSdwpq/rqkR1NQb6ffs5mZdU+5vz7xHuB4smDYhcAnyUJsjwL+EzgZOCQi3pA0Hvg+cGw69kBgeES8IGkwQES8ViTlfdt2zkHa51DgsiL1rY+IMcCHgacj4iNp/wGSNgd+BBwdEc+l5vg94JR07NYRMUbSIcDVwDDgkTLfTx/goxHxsqQdgPkpof5cYFhENKc6DgeGAqPJsglvS9d7FTgB2J/s72IJsLjjvw4zM+sp5TbCNRHRAiBpJXBPRISkFmAwMAC4VtJQsqijzQuOvTsiXijjGu2dA4CImEk2pVpKC3CJpIuA2yNiTsr5GwbcneIAm4BnCo65IZ17tqRtJQ0E+pf5fgR8PzW1TcAuwDuL1HV4+nkovd6GrDH2B26OiPUAqYkWJSfUm5lVRLmNcEPB8qaC15vSOb4LzIyIj6ZR36yC/V8t8xrtnQPoeEQYEY9JGkF27/EHkmaQZQyujIgDS1y3WEp9ue/nRGBHYEREvC7pCbJU+reVDvwgIn7W5v18scj1ixfphHozs4roqYdlBgBPpeVJZR6zjmxEVPY5ImJmieT4MfDP+KX1EXEdWQTTAcCjwI6SDkz7bC7pfQWnbb2POBZYGxFrO/F+BgDPpiZ4KNCafdj2vd0FnCJpm3StXSTtRBYG/FFJ/ST1B/6tnWuZmVkF9FQjvJhsBDaXbOqxHDOBfVofluniOdraF1ggaSnwdeCCiHgNOA64SNIysiT4MQXHvChpHnAlcGon38/1wEhJi8hGh48ARMTfgLnpAZzJETED+BXwQJpOng70j4glwLRU02+BOV1832Zm1kW9NqG+HJJmAWdHxKJa19IZ1Uio93eNmllvorwl1JuZmZUr1+kTETGu1jWYmVlt5boRNion1JuZ9RxPjZqZWa65EZqZWa65EZqZWa75HmEDanlqLYPPvaMi5/avTZhZ3nhEaGZmueZGaGZmuVbTRtiaT1jLGrpC0s6Spnfx2GZJDRtIbGbW23hE2EmS+kTE0xFxXBdP0UyWjmFmZnWgHhphk6SfS1opaUZKYmiWND+lud8s6R3wz0T5yyTNlvSwpFGSbpL0uKQLWk8o6SRJC9IXev9MUllf4i3pGklXSpoj6TFJR6b1bZPp/zmSlfRgYZpFqnGEiqTcS9oCOB+Y0Ppl45K2lnR1Sq9/SG+m3JuZWRXUQyMcCvwkIt4HvESWBD8V+GpEDCcL2/1Wwf6vRcQhZGkRtwJnkAXvTpK0vaT3kkUrHZQS4jeSJUMgaVpqQG1/Ti44/2Dgg8BHgCslteYLHghMjIjD2tT/a+Dj6fyDgJ0jYjFvptzvD5wHfD8lYZwHTEvxUdPIUjLujYhRwKHAZElbd/GzNDOzTqqHX59YExFL0/JiYAgwMCLuS+uuBW4s2L81xb2FLHD3GQBJq4FdgbHACGBhSqTvBzwLEBETyqjnNxGxCXg8nXPvtL4wmf4t+wN3kzXrjxfUOoDSKfeFDgeOknR2er0lsBvwcOFOTqg3M6uMemiEGwqWNwIDy9x/U5tjN5G9HwHXRsTX2h4oaRqwV5FzXhoRU9NyscR6eGsy/ZsbI56S9DdJw8lGop9Nm9pLuX9LWcCxEfFoie2t13FCvZlZBdTD1Ghba8nCcg9Orz8F3NfO/m3dAxyXEuCRtJ2k3SEbEZZIuJ9acPzxkjaTNAR4N1nCfUd+DXwFGBARLWldqZT7Yun1ZyoNXyXt34n3amZm3VSPjRBgItm9suVkT1meX+6BEbEK+AbZQy3LyaYtB3Xi2o+SNd7fA6dHxD/KOGY6cALZNGmrUin3M4F9Wh+WIRs5bg4sTw/gfLcTtZqZWTflOqG+LUnXALdHRJd+R7BaKplQ769YM7PeyAn1ZmZmJdTDwzJ1IyIm1boGMzOrLjfCBuSEejOznuOpUTMzyzU3QjMzyzU3QjMzyzXfI2xAlUio969NmFleeURoZma55kZoZma5VreNsDAFvtxUd0njJN1e+eo61pUU+5SH2NXAXzMz64K6bIRFUuCbqeNUd0l92r7uZoq9mZlVSY82wpTc/oikqyStkHS9pPGS5qYU+dHFktvTsUVT4Eukuhc9R0/XmvYrt972UuybJE1OKfTLJX02rZekH0taJekOYKee+HswM7PyVeKp0fcAx5OFyC4EPkkWlnsU8J/AyWTJ7W9IGg98nyyVHrIU+OER8ULK8CMiXpN0HjAyIj4PIGnbds5B2udQ4LIi9a2PiDFl1noMbybNd1TvpGL1J6cCayNilKS+wFxJM4D9yfIR9wXeCawCru7g8zUzsx5UiUa4pjWTT9JK4J6ICEktwGDaT24vlQLfVofp7xExk2xKtTu1dnSttvWWqv9wYHjB/b8BwFDgEOCGiNgIPC3p3lKFOqHezKwyKtEI26bGFybK96H95PaiKfBFdJj+XuaIsKNaO7pW23pL1S/gzIi4q02NR5A11w45od7MrDJq8bBMqeT29rRNde/wHBExs0Qa/Zhi+/dwvW3dBXxO0uYAkvaUtDUwGzgh3UMcBBzaxfObmVkX1aIRlkpub0/bVPeunKOreuJaV5Hd/1uSHqD5GdmI82bgcaAF+ClwX/fLNTOzznBCfQOqREK9v2LNzHozJ9SbmZmV4EZoZma55vSJBuSEejOznuMRoZmZ5ZoboZmZ5ZoboZmZ5ZrvETag7iTU+9ckzMzeyiNCMzPLNTdCMzPLtYZthIV5f71Bej+frHUdZmZ507CNsJ61Tawv02CyPEQzM6uiRm+ETZJ+LmmlpBmS+klqljQ/JcHfLOkdAJJmSbpM0mxJD0saJemmlEZ/QesJJZ0kaUH6gu+fSSrri7YlXSPpUkkzgYskDZF0p6TFkuZI2rtgvx+mtPvVBRmFFwIHp+t+qYc/JzMzK6HRG+FQ4CcR8T7gJbLk+KnAVyNiOFmqw7cK9n8tIg4BrgRuBc4AhgGTJG0v6b3ABOCgiGgGNgInAkialppU25+TC86/JzA+Iv6DLDvwzIgYAZwNXFGw3yBgLHAkWQMEOBeYk6KiiuUomplZBTT6r0+siYilaXkxMAQYGBGtcUbXAjcW7H9b+rMFWBkRzwBIWg3sStacRgALJQH0A54FiIgJZdRzY0RslLQNMAa4MZ0HoG/BfrdExCZglaR3lvNGnVBvZlYZjd4ICxPmNwIDy9y/MI2+9XUfsiT5ayPia20PlDQN2KvIOS+NiKlpuTWhfjPgpTSq7KhuldjnLZxQb2ZWGY3eCNtaC7wo6eCImAN8is6F3d4D3Crpsoh4VtJ2QP+IeLLMESEAEfGypDWSjo+IG5UNC4dHxLJ2DlsH9O9ErWZm1gMa/R5hMROByZKWA83A+eUeGBGrgG8AM9Lxd5Pdz+uKE4FTJS0DVgJHd7D/cuANScv8sIyZWfU4ob4BdSeh3l+xZmZ55IR6MzOzEtwIzcws13rbwzK54IR6M7Oe4xGhmZnlmhuhmZnlmhuhmZnlmu8RNqDOJNT71yXMzNrnEaGZmeWaG6GZmeWaG6GZmeVaXTVCSYMlrah1HWZmlh911QgtI6mp1jWYmeVFPTbCJkk/l7RS0gxJ/SQ1S5ovabmkmyW9A0DSLEmXSZot6WFJoyTdJOlxSRe0nlDSSZIWpET5n5XbaCTtLumedN17JO0mqUnSamUGStok6ZC0/xxJ75H0bUlXp/pWSzqro1okvSLpfEkPAgf26CdqZmYl1WMjHAr8JCLeB7wEHAtMBb4aEcPJ0uW/VbD/axFxCHAlcCtwBjAMmCRpe0nvBSYAB6Wg3I1kEUlImpYaUtufk9O5fwxMTde9HvhhRGwEHgP2IUu0XwwcLKkv8K6I+GM6dm/gX4DRwLckbd5eLcDWwIqIeH9E3N/2Q5F0mqRFkhZtXL+2a5+smZm9TT3+HuGaiFialhcDQ4CBEdEasHstcGPB/relP1uAlRHxDICk1cCuZM1qBLAwy8elH/AsQBlhuwcCH0vLvwQuTstzgEOAPYAfAJ8hCwBeWHDsHRGxAdgg6VngncCHStVC1hR/W6oQJ9SbmVVGPTbCDQXLG4GBZe6/qc2xm8jen4BrI+JrbQ+UNA3Yq8g5L42IqUXWtzagOcDpwM7AecA5wDhgdjvvo91agH+k0aaZmVVRPU6NtrUWeFHSwen1p8hGX+W6BzhO0k4AkraTtDtkI8KIaC7y09oE5wEnpOUTgdYpyweBMcCmiPgHsBT4LFmD7FItZmZWG/U4IixmInClpK2A1cCnyz0wIlZJ+gYwQ9JmwOtk9xGfLOPws4CrJZ0DPNd63YjYIOm/gflpvznAJ8imZytVi5mZVYAifLup0fQdNDQGTby8rH39XaNmZiBpcUSMLLatEaZGzczMKqZRpkatgBPqzcx6jkeEZmaWa26EZmaWa26EZmaWa75H2IDKSaj306JmZuXxiNDMzHLNjdDMzHLNjdDMzHKtbhphvaXTSzq9II7JzMx6KT8sU0JEXFnpa0hqcuKEmVlt1c2IMKmndPpvSzo7LY9K139A0uTWkaukSemad6brXlxw/CcktUhaIemigvVvSaLvan1mZtYz6q0R1lM6faFfAKdHxIHpHIWa0zX2BSZI2lXSzsBFwGFp+yhJx6T9/5lED/ytVH1tOaHezKwy6m1qtJ7S6UnnGgj0j4h5adWvgCMLdrknItamfVcBuwPbA7Mi4rm0/nqyRPtbeGsSfXuJ9W/hhHozs8qot0ZYj+n06mTNrdctpTCJvr3EejMzq4J6mxptq5bp9KT9XgTWSfpAWnUCHXsQ+KCkHdI9v0+UqNuJ9WZmNVZvI8JiapVOX+hU4OeSXgVmkTXo9q77jKSvATPJRn3/NyJurWB9ZmbWRU6oL4OkbSLilbR8LjAoIr5Qq3rKSaj3d42amb2pvYT6RhgR1oOPpBFeH7LR2qTalmNmZj3FjbAMETENmFbrOszMrOe5ETagfXcZwCJPfZqZ9Yh6f2rUzMysotwIzcws1zw12oDaS6j306JmZp3jEaGZmeWaG6GZmeWaG6GZmeVaXTRCSfPa2TZO0u1dPO+Okh6U9FDB95V2WcpALPrNBGZm1pjq4mGZiBhToVN/CHgkIia23eB0eDMzg/oZEb6izOSU6N4iqTAvcNuUTr9K0pXpC6o7OmczcDFwRArc7VduOrykw1Ma/RJJN0rapsj5f5qCcldK+k7B+ickfScd2yJp77R+G0m/SOuWSzq23GuZmVnl1EUjTD5Glua+HzAemCxpUNo2GvgPshT4IWnfdlPmU8DvecC0FK/0d8pIh5e0A/ANYHxEHAAsAr5cpN6vpy9wHU4WuTS8YNvz6difAmendd8E1kbEvhExHLi3E9cyM7MKqYup0WQscEOarvyrpPuAUcDLwIKIWA0g6Ya07/RyU+YLlJMO/wFgH2BuWr8F8ECRc31c0mlkn+GgdMzytO2m9OdiUtMma+7/zDKMiBclHVnmtUjXOg2gadsdO/GWzcysPfXUCNtLdW+bFRXQqZT5Vh2mw0v6N+DuiPhEyUKlPchGeqNSQ7sG2LJgl9bU+tbE+tbrtX0f6uharSJiCjAFshimjvY3M7Py1NPU6GxggqQmSTsChwAL0rbRkvZI9wYnAPdD+SnzJZRKh58PHCTpPWn9VpL2bHPstsCrwFpJ7wT+tYzrzQA+3/pC0jvKvJaZmVVQvTTCAG4mm1pcBtwLfCUi/pK2PwBcCKwA1qR9u3fBiFVk9+dmSFoO3E0WuPscWd7gDWn9fGDvNscuAx4CVgJXA3PLuOQFwDvSw0DLgEPLuZaZmVVWzRPqJW0PLImI3WtaSANpL6He3zVqZvZ27SXU13REKGlnstHeJbWsw8zM8qumD8tExNOA74mZmVnN1NNTo1YmJ9SbmfWcenlYxszMrCbcCM3MLNc8NdqAiiXU+2lRM7Ou8YjQzMxyzY3QzMxyzY3QzMxyra4boaTBklbUuo6OSDpG0j4Fr51kb2bWIOq6ETaQY8jilLqtNRzYzMyqoxEaYZOkn6ck+Bkpab5Z0vyU9H5zSnJoHYldJmm2pIcljZJ0k6THJV3QesJSyfQdkbS7pHvSde+RtJukMcBRZEHCSyUNSbsfn67xmKSD0/FNkiZLWpjO8dm0fpykmZJ+BbT05IdnZmbta4RGOBT4SUS8D3gJOBaYCnw1Jb23AN8q2P+1iDgEuBK4FTgDGAZMkrS9pPdSJJke2k+8T+f+MTA1Xfd64IcRMQ+4DTgnRUD9Ke3bJyJGA18sqO9UspT6UWShw59J2YYAo8lS73tkZGlmZuVphN8jXBMRS9PyYmAIMDAi7kvrrgVuLNj/tvRnC7AyIp4BkLQa2JUs3b5YMj1lJN4fyJuJ878ELm5n38KU+sFp+XBguKTj0usBZI3+NWBBRKwpdTIn1JuZVUYjNMINBcsbgYFl7r+pzbGbyN5v0WR66FLifXsZVqVS6s+MiLvaXHccWdBvSU6oNzOrjEaYGm1rLfBi63034FPAfe3s31apZPpyEu/nASek5ROB+9PyOqB/Gde+C/icpM3TtfeUtHUnajczsx7WCCPCYiYCV0raClgNfLrcAyNilaTWZPrNgNfJ7iM+WcbhZwFXSzoHeK7gur8Gfi7pLOC4UgcDV5FNky5RNi/7HNkTp2ZmViM1T6i3ziuWUO/vGjUzK61uE+rNzMxqzY3QzMxyrVHvEeaaE+rNzHqOR4RmZpZrboRmZpZrboRmZpZrboQNqOWptQw+945al2Fm1iu4EZqZWa65EZqZWa41fCOUNK+dbeMk3d7D13MavZlZL9LwjTAixlT5ksfgNHozs16j4RuhpFeUmSxphaQWSYW5gtumFPtVkq5MX7RdznmrkkYvaWtJd0halurvKBPRzMx6UG/5ZpmPAc3AfsAOZKG7s9O20WQjuCeBO9O+08vIHmxNo79W0ilkafTHSLoNuD0ipgOkcN8+ETFa0hFkafTjKUijl9QXmCtpRkFNwyJijaRjgacj4iPpfAN68HMxM7MO9JZGOBa4ISI2An+VdB8wCniZLPl9NYCkG9K+0+sojb4FuETSRWQNdk6xCzih3sysMnpLI1Q729rmTAXUTxp9RDwmaQRwBPADSTMi4vy3XdwJ9WZmFdHw9wiT2cCEdF9uR+AQYEHaNlrSHune4ARSqny9pNFL2hlYHxHXAZcAB3TyvZuZWTf0hhFhADeTTWUuS6+/EhF/kbQ38ABwIbAvWcO8uczzViuNfl+yh282Aa8DnyuzPjMz6wENnVAvaXtgSUTsXutaqqk1od6p9GZm5emVCfVpSvEBsulEMzOzLmnYqdGIeBrYs9Z1mJlZY2vYEWGe7bvLAE+Lmpn1EDdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNTdCMzPLNUVErWuwTpK0Dni01nUUsQPwfK2LKMG1dV691gX1W1u91gWubfeI2LHYhj4VvrBVxqMRMbLWRbQlaVE91gWurSvqtS6o39rqtS5wbe3x1KiZmeWaG6GZmeWaG2FjmlLrAkqo17rAtXVFvdYF9VtbvdYFrq0kPyxjZma55hGhmZnlmhthA5H0YUmPSvqjpHNrXMuukmZKeljSSklfSOu3k3S3pMfTn++oUX1Nkh6SdHud1TVQ0nRJj6TP7sB6qE3Sl9Lf4wpJN0jaslZ1Sbpa0rOSVhSsK1mLpK+lfxOPSvqXGtQ2Of19Lpd0s6SB9VJbwbazJYWkHapdW6m6JJ2Zrr1S0sXVrustIsI/DfADNAF/At4NbAEsA/apYT2DgAPScn/gMWAf4GLg3LT+XOCiGtX3ZeBXwO3pdb3UdS3wv9LyFsDAWtcG7AKsAfql178BJtWqLuAQ4ABgRcG6orWk/80tA/oCe6R/I01Vru1woE9avqieakvrdwXuAp4Edqh2bSU+s0OBPwB90+udavGZtf54RNg4RgN/jIjVEfEa8Gvg6FoVExHPRMSStLwOeJjsP6hHk/3HnvTnMdWuTdK7gI8AVxWsroe6tiX7j8J/AUTEaxHxUj3URvY7xf0k9QG2Ap6uVV0RMRt4oc3qUrUcDfw6IjZExBrgj2T/VqpWW0TMiIg30sv5wLvqpbbkMuArQOEDIVWrrURdnwMujIgNaZ9nq11XITfCxrEL8N8Fr/+c1tWcpMHA/sCDwDsj4hnImiWwUw1KupzsH/6mgnX1UNe7geeAX6Rp26skbV3r2iLiKeAS4P8BzwBrI2JGretqo1Qt9fbv4hTg92m55rVJOgp4KiKWtdlU69r2BA6W9KCk+ySNqmVdboSNQ0XW1fyRX0nbAL8FvhgRL9dBPUcCz0bE4lrXUkQfsimin0bE/sCrZNN8NZXutx1NNhW1M7C1pJNqW1XZ6ubfhaSvA28A17euKrJb1WqTtBXwdeC8YpuLrKvm59YHeAfwAeAc4DeSVKu63Agbx5/J5vpbvYts+qpmJG1O1gSvj4ib0uq/ShqUtg8Cni11fIUcBBwl6Qmy6ePDJF1XB3VB9nf454h4ML2eTtYYa13beGBNRDwXEa8DNwFj6qCuQqVqqYt/F5ImAkcCJ0a62VUHtQ0h+z83y9K/h3cBSyT9jzqo7c/ATZFZQDZ7s0Ot6nIjbBwLgaGS9pC0BXACcFutikn/7+2/gIcj4tKCTbcBE9PyRODWatYVEV+LiHdFxGCyz+jeiDip1nWl2v4C/LekvdKqDwGr6qC2/wd8QNJW6e/1Q2T3fGtdV6FStdwGnCCpr6Q9gKHAgmoWJunDwFeBoyJifcGmmtYWES0RsVNEDE7/Hv5M9oDbX2pdG3ALcBiApD3JHhx7vmZ1VfppHP/03A9wBNnTmX8Cvl7jWsaSTVksB5amnyOA7YF7gMfTn9vVsMZxvPnUaF3UBTQDi9LndgvZ9FDNawO+AzwCrAB+SfbUXk3qAm4gu1f5Otl/vE9trxay6b8/kSWy/GsNavsj2X2t1n8HV9ZLbW22P0F6arSatZX4zLYArkv/e1sCHFaLz6z1x98sY2ZmueapUTMzyzU3QjMzyzU3QjMzyzU3QjMzyzU3QjMzyzU3QjMzyzU3QjMzyzU3QjMzy7X/DwWIcnQjtdN6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 8))\n",
    "\n",
    "plt.barh(np.arange(len(scores)), scores)\n",
    "plt.yticks(np.arange(len(names)), names)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Extremely randomized trees\n",
    "\n",
    "Similar to random forest, but more random. Instead of selecting the best split among all posible splits, ET picks a few candidate splits at random and then select the best one among them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 -> 0.765\n",
      "20 -> 0.785\n",
      "30 -> 0.786\n",
      "40 -> 0.782\n",
      "50 -> 0.786\n",
      "60 -> 0.790\n",
      "70 -> 0.790\n",
      "80 -> 0.791\n",
      "90 -> 0.794\n",
      "100 -> 0.790\n",
      "110 -> 0.800\n",
      "120 -> 0.787\n",
      "130 -> 0.798\n",
      "140 -> 0.792\n",
      "150 -> 0.794\n",
      "160 -> 0.796\n",
      "170 -> 0.800\n",
      "180 -> 0.797\n",
      "190 -> 0.797\n",
      "200 -> 0.797\n"
     ]
    }
   ],
   "source": [
    "aucs = []\n",
    "\n",
    "for i in range(10, 201, 10):\n",
    "    rf = ExtraTreesClassifier(n_estimators=i, max_depth=30)\n",
    "    rf.fit(X_train, y_train)\n",
    "    y_pred = rf.predict_proba(X_val)[:, 1]\n",
    "    auc = roc_auc_score(y_val, y_pred)\n",
    "    print('%s -> %.3f' % (i, auc))\n",
    "    aucs.append(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
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