From a3fb013513de459381a6721a3d7fd43f6f4d26ed Mon Sep 17 00:00:00 2001
From: PAlena <86962990+PAlena@users.noreply.github.com>
Date: Mon, 29 Apr 2024 22:12:19 +0200
Subject: [PATCH] Add files via upload

---
 Seminar_5/DS_case_study_solved.ipynb | 1890 ++++++++++++++++++++++++++
 1 file changed, 1890 insertions(+)
 create mode 100644 Seminar_5/DS_case_study_solved.ipynb

diff --git a/Seminar_5/DS_case_study_solved.ipynb b/Seminar_5/DS_case_study_solved.ipynb
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@@ -0,0 +1,1890 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2d667870",
+   "metadata": {},
+   "source": [
+    "# Heart Attack Predicting Case Study"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be742eee",
+   "metadata": {},
+   "source": [
+    "## Problem definition: Predict whether a patient will have a heart attack or not"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c9c4ad8",
+   "metadata": {},
+   "source": [
+    "### Features:\n",
+    "Age : Age of the patient\n",
+    "\n",
+    "Sex : Sex of the patient\n",
+    "\n",
+    "exang: exercise induced angina (1 = yes; 0 = no)\n",
+    "\n",
+    "ca: number of major vessels (0-3)\n",
+    "\n",
+    "cp : Chest Pain type chest pain type\n",
+    "\n",
+    "Value 1: typical angina\n",
+    "Value 2: atypical angina\n",
+    "Value 3: non-anginal pain\n",
+    "Value 4: asymptomatic\n",
+    "trtbps : resting blood pressure (in mm Hg)\n",
+    "\n",
+    "chol : cholestoral in mg/dl fetched via BMI sensor\n",
+    "\n",
+    "fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
+    "\n",
+    "rest_ecg : resting electrocardiographic results\n",
+    "\n",
+    "Value 0: normal\n",
+    "Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
+    "Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
+    "thalach : maximum heart rate achieved\n",
+    "\n",
+    "target : 0= less chance of heart attack 1= more chance of heart attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "25294e0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import confusion_matrix\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cd1b4db",
+   "metadata": {},
+   "source": [
+    "## Data preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cad732a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"data/heart.csv\")\n",
+    "o2_saturation = pd.read_csv(\"data/o2Saturation.csv\").head(303)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "190ade33",
+   "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>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>cp</th>\n",
+       "      <th>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>fbs</th>\n",
+       "      <th>restecg</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>exng</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>slp</th>\n",
+       "      <th>caa</th>\n",
+       "      <th>thall</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>63</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>145</td>\n",
+       "      <td>233</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>150</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>130</td>\n",
+       "      <td>250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>187</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>130</td>\n",
+       "      <td>204</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>172</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>120</td>\n",
+       "      <td>236</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>178</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>57</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>354</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>163</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
+       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
+       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
+       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
+       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
+       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
+       "\n",
+       "   caa  thall  output  \n",
+       "0    0      1       1  \n",
+       "1    0      2       1  \n",
+       "2    0      2       1  \n",
+       "3    0      2       1  \n",
+       "4    0      2       1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9c2711c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 303 entries, 0 to 302\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   age       303 non-null    int64  \n",
+      " 1   sex       303 non-null    int64  \n",
+      " 2   cp        303 non-null    int64  \n",
+      " 3   trtbps    303 non-null    int64  \n",
+      " 4   chol      303 non-null    int64  \n",
+      " 5   fbs       303 non-null    int64  \n",
+      " 6   restecg   303 non-null    int64  \n",
+      " 7   thalachh  303 non-null    int64  \n",
+      " 8   exng      303 non-null    int64  \n",
+      " 9   oldpeak   303 non-null    float64\n",
+      " 10  slp       303 non-null    int64  \n",
+      " 11  caa       303 non-null    int64  \n",
+      " 12  thall     303 non-null    int64  \n",
+      " 13  output    303 non-null    int64  \n",
+      "dtypes: float64(1), int64(13)\n",
+      "memory usage: 33.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1489c727",
+   "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>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>cp</th>\n",
+       "      <th>trtbps</th>\n",
+       "      <th>chol</th>\n",
+       "      <th>fbs</th>\n",
+       "      <th>restecg</th>\n",
+       "      <th>thalachh</th>\n",
+       "      <th>exng</th>\n",
+       "      <th>oldpeak</th>\n",
+       "      <th>slp</th>\n",
+       "      <th>caa</th>\n",
+       "      <th>thall</th>\n",
+       "      <th>output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>303.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>54.366337</td>\n",
+       "      <td>0.683168</td>\n",
+       "      <td>0.966997</td>\n",
+       "      <td>131.623762</td>\n",
+       "      <td>246.264026</td>\n",
+       "      <td>0.148515</td>\n",
+       "      <td>0.528053</td>\n",
+       "      <td>149.646865</td>\n",
+       "      <td>0.326733</td>\n",
+       "      <td>1.039604</td>\n",
+       "      <td>1.399340</td>\n",
+       "      <td>0.729373</td>\n",
+       "      <td>2.313531</td>\n",
+       "      <td>0.544554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9.082101</td>\n",
+       "      <td>0.466011</td>\n",
+       "      <td>1.032052</td>\n",
+       "      <td>17.538143</td>\n",
+       "      <td>51.830751</td>\n",
+       "      <td>0.356198</td>\n",
+       "      <td>0.525860</td>\n",
+       "      <td>22.905161</td>\n",
+       "      <td>0.469794</td>\n",
+       "      <td>1.161075</td>\n",
+       "      <td>0.616226</td>\n",
+       "      <td>1.022606</td>\n",
+       "      <td>0.612277</td>\n",
+       "      <td>0.498835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>126.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>47.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>211.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>133.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>130.000000</td>\n",
+       "      <td>240.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>153.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>274.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>166.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>564.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>202.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              age         sex          cp      trtbps        chol         fbs  \\\n",
+       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
+       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
+       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
+       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
+       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
+       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
+       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
+       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
+       "\n",
+       "          restecg    thalachh        exng     oldpeak         slp         caa  \\\n",
+       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
+       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
+       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
+       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
+       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
+       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
+       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
+       "\n",
+       "            thall      output  \n",
+       "count  303.000000  303.000000  \n",
+       "mean     2.313531    0.544554  \n",
+       "std      0.612277    0.498835  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      2.000000    0.000000  \n",
+       "50%      2.000000    1.000000  \n",
+       "75%      3.000000    1.000000  \n",
+       "max      3.000000    1.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "17e69c68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# what is the distribution of age column?\n",
+    "data[\"age\"].hist(bins=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "caba3e33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numbers if man: 0.68\n",
+      "Numbers if woman: 0.32\n"
+     ]
+    }
+   ],
+   "source": [
+    "# What is the share of wemales in the sample? What is the share of males?\n",
+    "print(f'Numbers if man: {round(data.sex.sum()/len(data),2)}')\n",
+    "print(f'Numbers if woman: {round(1 - data.sex.sum()/len(data),2)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f0dd15a8-1502-40c5-8522-2b3737f89bac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add o2_saturation data in the data frame 'data'\n",
+    "# o2_saturation.rename(columns={'98.6':'o2_saturation'}, inplace = True)\n",
+    "# data = pd.concat([data, o2_saturation], axis=1)\n",
+    "data[\"o2_Saturation\"] = o2_saturation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "195ae311",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr = data.corr()\n",
+    "\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "sns.heatmap(corr, annot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "076fb645",
+   "metadata": {},
+   "source": [
+    "## Random Forest Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0fe76502",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify X and y and split the dataset\n",
+    "X = data.drop(\"output\", axis=1)\n",
+    "y = data[\"output\"]\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state  = 14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "183fd155",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((212, 14), (91, 14), (212,), (91,))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "077d86fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specify model\n",
+    "forest = RandomForestClassifier(random_state  = 14)\n",
+    "\n",
+    "# train model\n",
+    "forest.fit(X_train, y_train)\n",
+    "\n",
+    "# make prediction\n",
+    "forest_preds = forest.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "40e251ea-7fcd-4aee-b638-f29309b68b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n",
+       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
+       "       1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
+       "       1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
+       "       0, 1, 1])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest_preds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "080c566f-6f78-43d2-8782-008a738e0697",
+   "metadata": {},
+   "source": [
+    "## Evaluate the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "525bd174-6241-4d14-946a-f41fe31e6c7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "41\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Manually calculate number of True positive and False negative predictions\n",
+    "Y = pd.DataFrame()\n",
+    "Y['real'] = y_test\n",
+    "Y['pred'] = forest_preds\n",
+    "print(len(Y[(Y.real==1)&(Y.pred==1)]))\n",
+    "print(len(Y[(Y.real==1)&(Y.pred==0)]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e5252037-b250-4ae4-99c5-1138bfd537e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Confusion Matrix')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(confusion_matrix(y_test, forest_preds), annot=True)\n",
+    "plt.xlabel(\"Predicted Labels\")\n",
+    "plt.ylabel(\"Actual Labels\")\n",
+    "plt.title(\"Confusion Matrix\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3d3f1e98-c3cd-428d-aa14-d099db19e3a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Based on the confusion matrics fill:\n",
+    "true_positive = 41\n",
+    "true_negative = 33\n",
+    "false_positive = 9\n",
+    "false_negative = 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a5f55d8d-f413-4d0a-9b7b-23efc31c257d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.82\n",
+      "0.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Precision:  measure of how many of the positive predictions made are correct (true positives)\n",
+    "print(round(true_positive/(true_positive + false_positive),2))\n",
+    "print(round(sklearn.metrics.precision_score(y_test, forest_preds), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "979fb237-0771-4585-ae70-74a8b4db6077",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.84\n",
+      "0.84\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Recall:     is a measure of how many of the positive cases the classifier correctly predicted\n",
+    "print(round(true_positive/(true_positive + false_negative),2))\n",
+    "print(round(sklearn.metrics.recall_score(y_test, forest_preds), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "008cd394-6520-4888-863f-6463fe3fa9ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.83\n",
+      "0.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "# F1-Score:   is a measure combining both precision and recall 2(prec*recall)/(prec+recall)\n",
+    "print(round(2*(0.82*0.84)/(0.82+0.84),2))\n",
+    "print(round(sklearn.metrics.f1_score(y_test, forest_preds), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "c6fac62f-c5a8-4ba5-b7ee-82ec647b9ef0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.81\n",
+      "0.81\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Accuracy: describing the number of correct predictions over all predictions\n",
+    "print(round((true_positive+true_negative)/(true_positive+true_negative+false_positive+false_negative),2))\n",
+    "print(round(sklearn.metrics.accuracy_score(y_test, forest_preds), 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e43acac7-9bca-461c-9e32-8542a3fd04ef",
+   "metadata": {},
+   "source": [
+    "## Save the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "922aecda-8067-4b9c-8f2c-ad83e345b42a",
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>precision</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1_score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [precision, recall, f1_score, accuracy]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create empty df with indexes: ['precision','recall', 'f1_score','accuracy']\n",
+    "result = pd.DataFrame(index=['precision','recall', 'f1_score','accuracy'])\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1469766a-232b-4931-a7d6-43ee8b22141c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a function which will return 4 metrics:\n",
+    "# [round(sklearn.metrics.precision_score(y_test, forest_preds), 2),round(sklearn.metrics.recall_score(y_test, forest_preds), 2),round(sklearn.metrics.f1_score(y_test, forest_preds), 2),round(sklearn.metrics.accuracy_score(y_test, forest_preds), 2)]\n",
+    "\n",
+    "\n",
+    "def eval_result(x_predicted):\n",
+    "    return [round(sklearn.metrics.precision_score(y_test, x_predicted), 2),\n",
+    "            round(sklearn.metrics.recall_score(y_test, x_predicted), 2),\n",
+    "            round(sklearn.metrics.f1_score(y_test, x_predicted), 2),\n",
+    "            round(sklearn.metrics.accuracy_score(y_test, x_predicted), 2)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ce75bce6-17e4-46fc-bd25-c31bdc178d10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>precision</th>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall</th>\n",
+       "      <td>0.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1_score</th>\n",
+       "      <td>0.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           model_1\n",
+       "precision     0.82\n",
+       "recall        0.84\n",
+       "f1_score      0.83\n",
+       "accuracy      0.81"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# add the result of model_1 into the df 'result'\n",
+    "result['model_1'] = eval_result(forest_preds)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6c6db57-954c-4449-bc97-5c3ef07e2020",
+   "metadata": {},
+   "source": [
+    "### Change the threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cabae7b5-dc23-41bc-8cba-f9ba3e1df5ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 0, 0, 1])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest.predict(X_test)[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "bd4b584e-9b3d-47b2-ae9e-396c6f6af482",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.82, 0.18],\n",
+       "       [0.37, 0.63],\n",
+       "       [0.81, 0.19],\n",
+       "       [0.79, 0.21],\n",
+       "       [0.33, 0.67]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forest.predict_proba(X_test)[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0df4b875-4107-4ce8-837a-1c9b5bfcb669",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the result for a threshold = 0.7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "21754b98-1b7b-43f4-b2ad-18507ba9c86d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the result for any threshold from 0.1 till 0.9 and add it into 'result' df\n",
+    "res_proba = forest.predict_proba(X_test)\n",
+    "\n",
+    "for t in [x/10 for x in range(1, 10)]:\n",
+    "    res_pred = [0 if i[0]>=t else 1 for i in res_proba]\n",
+    "    result[f'model_thr_{t}'] = eval_result(res_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3b87475b-d862-44a7-a667-5962099483ee",
+   "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>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1_score</th>\n",
+       "      <th>accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.7</th>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_1</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.5</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.6</th>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.4</th>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.8</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.3</th>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.2</th>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.9</th>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.1</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.56</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               precision  recall  f1_score  accuracy\n",
+       "model_thr_0.7       0.75    1.00      0.86      0.82\n",
+       "model_1             0.82    0.84      0.83      0.81\n",
+       "model_thr_0.5       0.82    0.84      0.83      0.81\n",
+       "model_thr_0.6       0.78    0.92      0.84      0.81\n",
+       "model_thr_0.4       0.85    0.69      0.76      0.77\n",
+       "model_thr_0.8       0.69    1.00      0.82      0.76\n",
+       "model_thr_0.3       0.90    0.55      0.68      0.73\n",
+       "model_thr_0.2       0.96    0.47      0.63      0.70\n",
+       "model_thr_0.9       0.61    1.00      0.76      0.66\n",
+       "model_thr_0.1       1.00    0.18      0.31      0.56"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.T.sort_values('accuracy', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45d0fcc7",
+   "metadata": {},
+   "source": [
+    "### Using RandomizedSearchCV for hyperparameter turning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7c1501e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimator = RandomForestClassifier()\n",
+    "grid = {\"n_estimators\": [80, 90, 100, 110, 120],\n",
+    "        \"max_depth\": [5, 10, 15],\n",
+    "        \"max_features\" : [\"auto\", \"sqrt\", \"log2\"],\n",
+    "        \"min_samples_split\": [2, 1, 3, 4],\n",
+    "        \"min_samples_leaf\": [1, 2, 3, 4]}\n",
+    "\n",
+    "rand_search_model = RandomizedSearchCV(estimator=estimator,\n",
+    "                                      param_distributions=grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a9188e30",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
+      "5 fits failed out of a total of 50.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py\", line 476, in fit\n",
+      "    trees = Parallel(\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 1043, in __call__\n",
+      "    if self.dispatch_one_batch(iterator):\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
+      "    self._dispatch(tasks)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 779, in _dispatch\n",
+      "    job = self._backend.apply_async(batch, callback=cb)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
+      "    result = ImmediateResult(func)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
+      "    self.results = batch()\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 262, in __call__\n",
+      "    return [func(*args, **kwargs)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
+      "    return [func(*args, **kwargs)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
+      "    return self.function(*args, **kwargs)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py\", line 189, in _parallel_build_trees\n",
+      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/tree/_classes.py\", line 969, in fit\n",
+      "    super().fit(\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/tree/_classes.py\", line 265, in fit\n",
+      "    check_scalar(\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py\", line 1480, in check_scalar\n",
+      "    raise ValueError(\n",
+      "ValueError: min_samples_split == 1, must be >= 2.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.81550388 0.8013289  0.81572536 0.82026578        nan 0.81539313\n",
+      " 0.82026578 0.81085271 0.82026578 0.81096346]\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Retrain the model with tuned parameters and compare with model 1\n",
+    "rand_search_model.fit(X_train, y_train)\n",
+    "rand_search_pred = rand_search_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "acbb111b-8b61-46b2-92b3-511588758d0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_estimators': 90,\n",
+       " 'min_samples_split': 3,\n",
+       " 'min_samples_leaf': 3,\n",
+       " 'max_features': 'log2',\n",
+       " 'max_depth': 10}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rand_search_model.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "10b109c8",
+   "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>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1_score</th>\n",
+       "      <th>accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.7</th>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_1</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.5</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.6</th>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tuned_model</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.4</th>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.8</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.3</th>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.2</th>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.9</th>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.1</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.56</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               precision  recall  f1_score  accuracy\n",
+       "model_thr_0.7       0.75    1.00      0.86      0.82\n",
+       "model_1             0.82    0.84      0.83      0.81\n",
+       "model_thr_0.5       0.82    0.84      0.83      0.81\n",
+       "model_thr_0.6       0.78    0.92      0.84      0.81\n",
+       "tuned_model         0.82    0.82      0.82      0.80\n",
+       "model_thr_0.4       0.85    0.69      0.76      0.77\n",
+       "model_thr_0.8       0.69    1.00      0.82      0.76\n",
+       "model_thr_0.3       0.90    0.55      0.68      0.73\n",
+       "model_thr_0.2       0.96    0.47      0.63      0.70\n",
+       "model_thr_0.9       0.61    1.00      0.76      0.66\n",
+       "model_thr_0.1       1.00    0.18      0.31      0.56"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['tuned_model'] = eval_result(rand_search_pred)\n",
+    "result.T.sort_values('accuracy', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b084c123",
+   "metadata": {},
+   "source": [
+    "## Feature importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "44066cb9-2bcc-4450-935b-8c5fb86d48b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importances = forest.feature_importances_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b86599aa-8ef2-49c8-8aea-1674a1a9bc5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "forest_importances = pd.Series(importances, index = X.columns)\n",
+    "forest_importances = forest_importances.sort_values(ascending=False)\n",
+    "fig, ax = plt.subplots()\n",
+    "forest_importances.plot.bar(ax=ax)\n",
+    "ax.set_title(\"Feature importances\")\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "1526680d-a94a-4335-8644-902d6a5751b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop the least important feature and compare the result with model 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "046d5bab-4f5b-47bc-b7c0-080359042bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test.drop(['fbs'], axis=1, inplace=True)\n",
+    "X_train.drop(['fbs'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "2f730335-849d-4fbd-b148-01fe746e2721",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
+      "5 fits failed out of a total of 50.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "5 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py\", line 476, in fit\n",
+      "    trees = Parallel(\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 1043, in __call__\n",
+      "    if self.dispatch_one_batch(iterator):\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
+      "    self._dispatch(tasks)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 779, in _dispatch\n",
+      "    job = self._backend.apply_async(batch, callback=cb)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
+      "    result = ImmediateResult(func)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
+      "    self.results = batch()\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 262, in __call__\n",
+      "    return [func(*args, **kwargs)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
+      "    return [func(*args, **kwargs)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/utils/fixes.py\", line 117, in __call__\n",
+      "    return self.function(*args, **kwargs)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py\", line 189, in _parallel_build_trees\n",
+      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/tree/_classes.py\", line 969, in fit\n",
+      "    super().fit(\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/tree/_classes.py\", line 265, in fit\n",
+      "    check_scalar(\n",
+      "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py\", line 1480, in check_scalar\n",
+      "    raise ValueError(\n",
+      "ValueError: min_samples_split == 1, must be >= 2.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.8393134  0.81085271 0.82026578 0.79667774        nan 0.82015504\n",
+      " 0.82513843 0.81085271 0.80586932 0.82048726]\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.8/dist-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
+      "  warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "feat_model = rand_search_model.fit(X_train, y_train)\n",
+    "feat_pred = feat_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "2ff087f3-9c1d-4860-a3f1-069cc2c61dce",
+   "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>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1_score</th>\n",
+       "      <th>accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.7</th>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_1</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.5</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.6</th>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tuned_model</th>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>features_tuned_model</th>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.4</th>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.8</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.3</th>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.2</th>\n",
+       "      <td>0.96</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.9</th>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_thr_0.1</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.31</td>\n",
+       "      <td>0.56</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      precision  recall  f1_score  accuracy\n",
+       "model_thr_0.7              0.75    1.00      0.86      0.82\n",
+       "model_1                    0.82    0.84      0.83      0.81\n",
+       "model_thr_0.5              0.82    0.84      0.83      0.81\n",
+       "model_thr_0.6              0.78    0.92      0.84      0.81\n",
+       "tuned_model                0.82    0.82      0.82      0.80\n",
+       "features_tuned_model       0.80    0.84      0.82      0.80\n",
+       "model_thr_0.4              0.85    0.69      0.76      0.77\n",
+       "model_thr_0.8              0.69    1.00      0.82      0.76\n",
+       "model_thr_0.3              0.90    0.55      0.68      0.73\n",
+       "model_thr_0.2              0.96    0.47      0.63      0.70\n",
+       "model_thr_0.9              0.61    1.00      0.76      0.66\n",
+       "model_thr_0.1              1.00    0.18      0.31      0.56"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['features_tuned_model'] = eval_result(feat_pred)\n",
+    "result.T.sort_values('accuracy', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f57a68eb-efd5-41e8-8348-bffa40f1c2c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}