Exercise - Decision Tree Akshit Ram Pershad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise - Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data set for this exercise is from healthcare. It contains data about 10 years of clinical care at 130 US Hospitals. Each row represents a single patient. The columns include the characteristics of deidentified diabetes patients. This is a binary classification task: predict whether a diabetes patient is readmitted to the hospital within 30 days of their discharge (1=Yes, 0=No). This is an important performance metric for hospitals as they try to minimize these types of readmissions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of Variables\n",
    "\n",
    "The description of variables are provided in \"Healthcare (small) - Data Dictionary.docx\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal\n",
    "\n",
    "Use the **healthcare_small.csv** data set and build a model to predict **readmitted**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read and Prepare the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common imports\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type</th>\n",
       "      <th>discharge_disposition</th>\n",
       "      <th>admission_source</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>70-80</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
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       "      <td>3</td>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>80-90</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Male</td>\n",
       "      <td>50-60</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>50-60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-50</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              race  gender    age  admission_type  discharge_disposition  \\\n",
       "0            Other  Female  70-80               2                      3   \n",
       "1        Caucasian  Female  80-90               1                      3   \n",
       "2  AfricanAmerican    Male  50-60               5                      1   \n",
       "3        Caucasian  Female  50-60               1                      1   \n",
       "4  AfricanAmerican  Female  40-50               3                      1   \n",
       "\n",
       "   admission_source  time_in_hospital  num_lab_procedures  num_procedures  \\\n",
       "0                 1                14                  32               3   \n",
       "1                 5                 4                  44               0   \n",
       "2                 1                 6                  29               1   \n",
       "3                 6                 3                  47               0   \n",
       "4                 1                 4                  92               0   \n",
       "\n",
       "   num_medications  number_outpatient  number_emergency  number_inpatient  \\\n",
       "0               15                  0                 0                 0   \n",
       "1               15                  0                 0                 0   \n",
       "2               15                  0                 0                 0   \n",
       "3               10                  0                 0                 0   \n",
       "4               15                  0                 0                 0   \n",
       "\n",
       "   number_diagnoses  readmitted  \n",
       "0                 9           1  \n",
       "1                 9           0  \n",
       "2                 9           1  \n",
       "3                 4           0  \n",
       "4                 7           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will predict the \"price\" value in the data set:\n",
    "\n",
    "diabetes = pd.read_csv(\"healthcare_small.csv\")\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data (train/test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(diabetes, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import FunctionTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separate the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_target = train['readmitted']\n",
    "test_target = test['readmitted']\n",
    "\n",
    "train_inputs = train.drop(['readmitted'], axis=1)\n",
    "test_inputs = test.drop(['readmitted'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering: Derive a new column\n",
    "\n",
    "Examples:\n",
    "- Whether the patient had any emergency visits or not\n",
    "- Ratio of inpatient visits to outpatient visits\n",
    "- Ratio of time in hospital to number of medications\n",
    "- (etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_col(df):\n",
    "    \n",
    "    #Create a copy so that we don't overwrite the existing dataframe\n",
    "    df1 = df.copy()\n",
    "\n",
    "    # Use the formula, though fill in 0s when the value is 0/0 (because 0/0 generates \"nan\" values)\n",
    "    df1['time_in_hospital'] = (df1['time_in_hospital']/df1['num_medications']).fillna(0)\n",
    "\n",
    "    # Replace the infinity values with 1 (because a value divided by 0 generates infinity)\n",
    "    df1['time_in_hospital'].replace(np.inf, 1, inplace=True)\n",
    "\n",
    "    return df1[['time_in_hospital']]\n",
    "    # You can use this to check whether the calculation is made correctly:\n",
    "    #return df1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We use Future Engineering to extract variables from the dataset and helps to create models with better accuracy values of train and test. \n",
    "\n",
    "#### From the above code we can observe, here we have created an new dataframe new_col(df)\n",
    "d1['time_on_hospital'] = (df1['num_medications']/d1['num_medications']\n",
    "\n",
    "#### Both time_on_hospital and num_medications are numeric variable.\n",
    "\n",
    "#### Now, If the formula (time_on_hospital \\ num_medication) sastifies the given condition the result will be a 0 if it doesn't satisfy, the result it will be replaced by 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>time_in_hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4166</th>\n",
       "      <td>0.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5546</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6329</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>0.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>0.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6066 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      time_in_hospital\n",
       "4166          0.210526\n",
       "5546          0.500000\n",
       "2957          0.105263\n",
       "6329          0.200000\n",
       "565           0.250000\n",
       "...                ...\n",
       "5734          0.322581\n",
       "5191          0.153846\n",
       "5390          0.608696\n",
       "860           0.071429\n",
       "7270          0.263158\n",
       "\n",
       "[6066 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's test the new function:\n",
    "\n",
    "# Send the train set to the function we created\n",
    "new_col(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Identify the numeric, binary, and categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's change some of the categorical variables' data type to object\n",
    "\n",
    "train_inputs['admission_source'] = train_inputs['admission_source'].astype('object')\n",
    "train_inputs['admission_type'] = train_inputs['admission_type'].astype('object')\n",
    "train_inputs['discharge_disposition'] = train_inputs['discharge_disposition'].astype('object')\n",
    "\n",
    "test_inputs['admission_source'] = test_inputs['admission_source'].astype('object')\n",
    "test_inputs['admission_type'] = test_inputs['admission_type'].astype('object')\n",
    "test_inputs['discharge_disposition'] = test_inputs['discharge_disposition'].astype('object')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify the numerical columns\n",
    "numeric_columns = train_inputs.select_dtypes(include=[np.number]).columns.to_list()\n",
    "\n",
    "# Identify the categorical columns\n",
    "categorical_columns = train_inputs.select_dtypes('object').columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['time_in_hospital',\n",
       " 'num_lab_procedures',\n",
       " 'num_procedures',\n",
       " 'num_medications',\n",
       " 'number_outpatient',\n",
       " 'number_emergency',\n",
       " 'number_inpatient',\n",
       " 'number_diagnoses']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['race',\n",
       " 'gender',\n",
       " 'age',\n",
       " 'admission_type',\n",
       " 'discharge_disposition',\n",
       " 'admission_source']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_eng_columns = ['time_in_hospital', 'num_medications' ]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_transformer = Pipeline(steps=[\n",
    "                ('imputer', SimpleImputer(strategy='median')),\n",
    "                ('scaler', StandardScaler())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value='unknown')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a pipeline for the transformed column here\n",
    "my_new_column = Pipeline(steps=[('my_new_column', FunctionTransformer(new_col)),\n",
    "                               ('scaler', StandardScaler())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = ColumnTransformer([\n",
    "        ('num', numeric_transformer, numeric_columns),\n",
    "        ('cat', categorical_transformer, categorical_columns),\n",
    "        ('trans', my_new_column, feat_eng_columns)],   \n",
    "        remainder='passthrough')\n",
    "\n",
    "#passtrough is an optional step. You don't have to use it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform: fit_transform() for TRAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<6066x65 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 90990 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit and transform the train data\n",
    "train_x = preprocessor.fit_transform(train_inputs)\n",
    "\n",
    "train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6066, 65)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tranform: transform() for TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2600x65 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 38997 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform the test data\n",
    "test_x = preprocessor.transform(test_inputs)\n",
    "\n",
    "test_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2600, 65)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier(strategy=&#x27;most_frequent&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DummyClassifier(strategy='most_frequent')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n",
    "\n",
    "dummy_clf.fit(train_x, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Train Accuracy: 0.5313221233102539\n"
     ]
    }
   ],
   "source": [
    "#Baseline Train Accuracy\n",
    "dummy_train_pred = dummy_clf.predict(train_x)\n",
    "\n",
    "baseline_train_acc = accuracy_score(train_target, dummy_train_pred)\n",
    "\n",
    "print('Baseline Train Accuracy: {}' .format(baseline_train_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Test Accuracy: 0.5365384615384615\n"
     ]
    }
   ],
   "source": [
    "#Baseline Test Accuracy\n",
    "dummy_test_pred = dummy_clf.predict(test_x)\n",
    "\n",
    "baseline_test_acc = accuracy_score(test_target, dummy_test_pred)\n",
    "\n",
    "print('Baseline Test Accuracy: {}' .format(baseline_test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a DT model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=5)\n",
    "\n",
    "tree_clf.fit(train_x, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6264424662050775\n"
     ]
    }
   ],
   "source": [
    "#Train accuracy:\n",
    "train_y_pred = tree_clf.predict(train_x)\n",
    "\n",
    "print(accuracy_score(train_target, train_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.605\n"
     ]
    }
   ],
   "source": [
    "#Test accuracy:\n",
    "test_y_pred = tree_clf.predict(test_x)\n",
    "\n",
    "print(accuracy_score(test_target, test_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.008, 0.034, 0.   , 0.025, 0.   , 0.   , 0.545, 0.018, 0.   ,\n",
       "       0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.006,\n",
       "       0.   , 0.   , 0.   , 0.022, 0.   , 0.   , 0.   , 0.005, 0.   ,\n",
       "       0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.037, 0.   , 0.054,\n",
       "       0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.08 , 0.   , 0.   ,\n",
       "       0.   , 0.   , 0.   , 0.113, 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
       "       0.   , 0.   , 0.   , 0.   , 0.   , 0.013, 0.   , 0.   , 0.   ,\n",
       "       0.   , 0.041])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are 65 columns, so there are 65 values for each column's importance\n",
    "# Values are rounded to three decimals\n",
    "\n",
    "np.round(tree_clf.feature_importances_,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.041"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The importance of the last column (i.e., the one we derived)\n",
    "\n",
    "np.round(tree_clf.feature_importances_,3)[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"numpy.int64\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [35]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m125\u001b[39m,\u001b[38;5;241m50\u001b[39m))\n\u001b[1;32m----> 6\u001b[0m tree \u001b[38;5;241m=\u001b[39m \u001b[43mplot_tree\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtree_clf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m              \u001b[49m\u001b[38;5;66;43;03m#feature_names=train_inputs.columns.values, # our feature names are stripped from the data set\u001b[39;49;00m\n\u001b[0;32m      8\u001b[0m \u001b[43m              \u001b[49m\u001b[43mclass_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_target\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m              \u001b[49m\u001b[43mfilled\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m              \u001b[49m\u001b[43mrounded\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m              \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m14\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_export.py:194\u001b[0m, in \u001b[0;36mplot_tree\u001b[1;34m(decision_tree, max_depth, feature_names, class_names, label, filled, impurity, node_ids, proportion, rounded, precision, ax, fontsize)\u001b[0m\n\u001b[0;32m    179\u001b[0m check_is_fitted(decision_tree)\n\u001b[0;32m    181\u001b[0m exporter \u001b[38;5;241m=\u001b[39m _MPLTreeExporter(\n\u001b[0;32m    182\u001b[0m     max_depth\u001b[38;5;241m=\u001b[39mmax_depth,\n\u001b[0;32m    183\u001b[0m     feature_names\u001b[38;5;241m=\u001b[39mfeature_names,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    192\u001b[0m     fontsize\u001b[38;5;241m=\u001b[39mfontsize,\n\u001b[0;32m    193\u001b[0m )\n\u001b[1;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mexporter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecision_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_export.py:655\u001b[0m, in \u001b[0;36m_MPLTreeExporter.export\u001b[1;34m(self, decision_tree, ax)\u001b[0m\n\u001b[0;32m    653\u001b[0m ax\u001b[38;5;241m.\u001b[39mclear()\n\u001b[0;32m    654\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_axis_off()\n\u001b[1;32m--> 655\u001b[0m my_tree \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_tree\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecision_tree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecision_tree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    656\u001b[0m draw_tree \u001b[38;5;241m=\u001b[39m buchheim(my_tree)\n\u001b[0;32m    658\u001b[0m \u001b[38;5;66;03m# important to make sure we're still\u001b[39;00m\n\u001b[0;32m    659\u001b[0m \u001b[38;5;66;03m# inside the axis after drawing the box\u001b[39;00m\n\u001b[0;32m    660\u001b[0m \u001b[38;5;66;03m# this makes sense because the width of a box\u001b[39;00m\n\u001b[0;32m    661\u001b[0m \u001b[38;5;66;03m# is about the same as the distance between boxes\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_export.py:631\u001b[0m, in \u001b[0;36m_MPLTreeExporter._make_tree\u001b[1;34m(self, node_id, et, criterion, depth)\u001b[0m\n\u001b[0;32m    628\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_make_tree\u001b[39m(\u001b[38;5;28mself\u001b[39m, node_id, et, criterion, depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[0;32m    629\u001b[0m     \u001b[38;5;66;03m# traverses _tree.Tree recursively, builds intermediate\u001b[39;00m\n\u001b[0;32m    630\u001b[0m     \u001b[38;5;66;03m# \"_reingold_tilford.Tree\" object\u001b[39;00m\n\u001b[1;32m--> 631\u001b[0m     name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_to_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43met\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    632\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m et\u001b[38;5;241m.\u001b[39mchildren_left[node_id] \u001b[38;5;241m!=\u001b[39m _tree\u001b[38;5;241m.\u001b[39mTREE_LEAF \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[0;32m    633\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_depth \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m depth \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_depth\n\u001b[0;32m    634\u001b[0m     ):\n\u001b[0;32m    635\u001b[0m         children \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    636\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_tree(\n\u001b[0;32m    637\u001b[0m                 et\u001b[38;5;241m.\u001b[39mchildren_left[node_id], et, criterion, depth\u001b[38;5;241m=\u001b[39mdepth \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    641\u001b[0m             ),\n\u001b[0;32m    642\u001b[0m         ]\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_export.py:373\u001b[0m, in \u001b[0;36m_BaseTreeExporter.node_to_str\u001b[1;34m(self, tree, node_id, criterion)\u001b[0m\n\u001b[0;32m    367\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    368\u001b[0m         class_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\n\u001b[0;32m    369\u001b[0m             characters[\u001b[38;5;241m1\u001b[39m],\n\u001b[0;32m    370\u001b[0m             np\u001b[38;5;241m.\u001b[39margmax(value),\n\u001b[0;32m    371\u001b[0m             characters[\u001b[38;5;241m2\u001b[39m],\n\u001b[0;32m    372\u001b[0m         )\n\u001b[1;32m--> 373\u001b[0m     node_string \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m class_name\n\u001b[0;32m    375\u001b[0m \u001b[38;5;66;03m# Clean up any trailing newlines\u001b[39;00m\n\u001b[0;32m    376\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m node_string\u001b[38;5;241m.\u001b[39mendswith(characters[\u001b[38;5;241m4\u001b[39m]):\n",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"numpy.int64\") to str"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 9000x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(125,50))\n",
    "\n",
    "tree = plot_tree(tree_clf, \n",
    "              #feature_names=train_inputs.columns.values, # our feature names are stripped from the data set\n",
    "              class_names=np.unique(train_target), \n",
    "              filled=True, \n",
    "              rounded=True, \n",
    "              fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train another DT model (with different parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "tree_clf2 = DecisionTreeClassifier(min_samples_leaf = 10, max_depth=5)\n",
    "\n",
    "tree_clf2.fit(train_x, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train accuracy:\n",
    "train_y_pred = tree_clf2.predict(train_x)\n",
    "\n",
    "print(accuracy_score(train_target, train_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test accuracy:\n",
    "test_y_pred = tree_clf2.predict(test_x)\n",
    "\n",
    "print(accuracy_score(test_target, test_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are 65 columns, so there are 65 values for each column's importance\n",
    "# Values are rounded to three decimals\n",
    "\n",
    "np.round(tree_clf.feature_importances_,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The importance of the last column (i.e., the one we derived)\n",
    "\n",
    "np.round(tree_clf.feature_importances_,3)[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optional: try grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_grid = {'max_depth': randint(low=5, high=20), \n",
    "              'min_samples_leaf': randint(low=5, high=20)}\n",
    "\n",
    "tree_gs = RandomizedSearchCV(DecisionTreeClassifier(), param_grid, \n",
    "                             n_iter=15, cv=5, verbose=1,\n",
    "                             scoring='accuracy',\n",
    "                             return_train_score=True)\n",
    "\n",
    "tree_gs.fit(train_x, train_target)g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvres = tree_gs.cv_results_\n",
    "\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(mean_score, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find the best parameter set\n",
    "tree_gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train accuracy:\n",
    "train_y_pred = tree_gs.best_estimator_.predict(train_x)\n",
    "\n",
    "print(accuracy_score(train_target, train_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test accuracy:\n",
    "test_y_pred = tree_gs.best_estimator_.predict(test_x)\n",
    "\n",
    "print(accuracy_score(test_target, test_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "#Test confusion matrix\n",
    "confusion_matrix(test_target, test_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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