{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c5ba4fe",
   "metadata": {},
   "source": [
    "# Decision Trees - Intro and Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07494ffb",
   "metadata": {},
   "source": [
    "Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets. \n",
    "\n",
    "Here are 7 interesting facts about decision trees:\n",
    "\n",
    "* They do not need the numerical input data to be scaled. Whatever the numerical values are, decision trees don't care. \n",
    "\n",
    "* Decision trees handle categorical features in the raw text format (Scikit-Learn doesn't support this, TensorFlow's trees implementation does).\n",
    "\n",
    "* Different to other complex learning algorithms, the results of decision trees can be interpreted. It's fair to say that decision trees are not blackbox type models. \n",
    "* While most models will suffer from missing values, decision trees are okay with them.\n",
    "* Trees can handle imbalanced datasets. You will only have to adjust the weights of the classes.\n",
    "* Trees can provide the feature importances or how much each feature contributed to the model training results.\n",
    "* Trees are the basic building blocks of ensemble methods such as random forests and gradient boosting machines.\n",
    "\n",
    "The way decision trees works is like the series of if/else questions. Let's say that you want to make a decision of the car to buy. In order to get the right car to buy, you could go on and evaluate the level of the safety, the number of sits and doors by asking series of if like questions. \n",
    "\n",
    "Here is the structure of the decision trees.\n",
    "\n",
    "![Decision Trees.png](../../../../images/ml-fundamentals/4XijLSXib.png)\n",
    "\n",
    "\n",
    "A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as `max_depth`hyperparameter) in decision trees. We will see other techniques to avoid overfitting. \n",
    "\n",
    "To motivate the superpower of decision trees, let's use it for a regression task where instead of predicting class, we are predicting a continous value. In the next lab, we will use them for classification. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e2c5a6",
   "metadata": {},
   "source": [
    "## Decision Trees for Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e8372",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* [1 - Imports]\n",
    "* [2 - Loading the data]\n",
    "* [3 - Exploratory Analysis]\n",
    "* [4 - Preprocessing the data]\n",
    "* [5 - Training Decision Trees]\n",
    "* [6 - Evaluating Decision Trees]\n",
    "* [7 - Improving Decision Trees]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99058d12",
   "metadata": {},
   "source": [
    "## 1 - Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df278c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35699c38",
   "metadata": {},
   "source": [
    "## 2 - Loading the data\n",
    "\n",
    "In this regression task with decision trees, we will use the Machine CPU (Central Processing Unit) data which is avilable at [OpenML](https://www.openml.org/t/5492). We will load it with Sklearn `fetch_openml` function. \n",
    "\n",
    "If you are reading this, it's very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data: \n",
    "\n",
    "* MYCT: machine cycle time in nanoseconds (integer)\n",
    "* MMIN: minimum main memory in kilobytes (integer)\n",
    "* MMAX: maximum main memory in kilobytes (integer)\n",
    "* CACH: cache memory in kilobytes (integer)\n",
    "* CHMIN: minimum channels in units (integer)\n",
    "* CHMAX: maximum channels in units (integer)\n",
    "* PRP: published relative performance (integer) (target variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67905ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's hide warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3083946",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "machine_cpu = fetch_openml(name='machine_cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46c09852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils._bunch.Bunch"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_cpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "154ed67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(209, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machine_cpu.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69e438fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Author**:   \n",
      "**Source**: Unknown -   \n",
      "**Please cite**:   \n",
      "\n",
      "The problem concerns Relative CPU Performance Data. More information can be obtained in the UCI Machine\n",
      " Learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).\n",
      " The used attributes are :\n",
      " MYCT: machine cycle time in nanoseconds (integer)\n",
      " MMIN: minimum main memory in kilobytes (integer)\n",
      " MMAX: maximum main memory in kilobytes (integer)\n",
      " CACH: cache memory in kilobytes (integer)\n",
      " CHMIN: minimum channels in units (integer)\n",
      " CHMAX: maximum channels in units (integer)\n",
      " PRP: published relative performance (integer) (target variable)\n",
      " \n",
      " Original source: UCI machine learning repository. \n",
      " Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at\n",
      " http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html\n",
      " Characteristics: 209 cases; 6 continuous variables\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "print(machine_cpu.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7180991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN', 'CHMAX']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying feature names\n",
    "\n",
    "machine_cpu.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3ba092e",
   "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>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>125.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>124.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>98.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>125.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>480.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>480.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MYCT    MMIN     MMAX   CACH  CHMIN  CHMAX  class\n",
       "0    125.0   256.0   6000.0  256.0   16.0  128.0  198.0\n",
       "1     29.0  8000.0  32000.0   32.0    8.0   32.0  269.0\n",
       "2     29.0  8000.0  32000.0   32.0    8.0   32.0  220.0\n",
       "3     29.0  8000.0  32000.0   32.0    8.0   32.0  172.0\n",
       "4     29.0  8000.0  16000.0   32.0    8.0   16.0  132.0\n",
       "..     ...     ...      ...    ...    ...    ...    ...\n",
       "204  124.0  1000.0   8000.0    0.0    1.0    8.0   42.0\n",
       "205   98.0  1000.0   8000.0   32.0    2.0    8.0   46.0\n",
       "206  125.0  2000.0   8000.0    0.0    2.0   14.0   52.0\n",
       "207  480.0   512.0   8000.0   32.0    0.0    0.0   67.0\n",
       "208  480.0  1000.0   4000.0    0.0    0.0    0.0   45.0\n",
       "\n",
       "[209 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the whole dataframe\n",
    "\n",
    "machine_cpu.frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b6b4ae",
   "metadata": {},
   "source": [
    "Now, let's get the data and labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59f873c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "machine_data = machine_cpu.data\n",
    "machine_labels = machine_cpu.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef5bf335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f05b049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(machine_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c492fd93",
   "metadata": {},
   "source": [
    "## 3 - Exploratory Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9906083",
   "metadata": {},
   "source": [
    "Before doing exploratory analysis, let's get the training and test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98d74144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training data is: 167 \n",
      "The size of testing data is: 42\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(machine_data,machine_labels, test_size=0.2,random_state=20)\n",
    "\n",
    "print('The size of training data is: {} \\nThe size of testing data is: {}'.format(len(X_train), len(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fc640e",
   "metadata": {},
   "source": [
    "Let's visualize the histograms of all numeric features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04856278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train.hist(bins=50, figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eca7747",
   "metadata": {},
   "source": [
    "Or we can quickly use `sns.pairplot()` to look into the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "803a1d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x227c78bf790>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "753bb539",
   "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>MYCT</th>\n",
       "      <th>MMIN</th>\n",
       "      <th>MMAX</th>\n",
       "      <th>CACH</th>\n",
       "      <th>CHMIN</th>\n",
       "      <th>CHMAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>207.958084</td>\n",
       "      <td>2900.826347</td>\n",
       "      <td>11761.161677</td>\n",
       "      <td>26.071856</td>\n",
       "      <td>4.760479</td>\n",
       "      <td>18.616766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>266.772823</td>\n",
       "      <td>4165.950964</td>\n",
       "      <td>12108.332354</td>\n",
       "      <td>42.410014</td>\n",
       "      <td>6.487439</td>\n",
       "      <td>27.489919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.000000</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>50.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>232.500000</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>176.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MYCT          MMIN          MMAX        CACH       CHMIN  \\\n",
       "count   167.000000    167.000000    167.000000  167.000000  167.000000   \n",
       "mean    207.958084   2900.826347  11761.161677   26.071856    4.760479   \n",
       "std     266.772823   4165.950964  12108.332354   42.410014    6.487439   \n",
       "min      17.000000     64.000000     64.000000    0.000000    0.000000   \n",
       "25%      50.000000    768.000000   4000.000000    0.000000    1.000000   \n",
       "50%     110.000000   2000.000000   8000.000000    8.000000    2.000000   \n",
       "75%     232.500000   3100.000000  16000.000000   32.000000    6.000000   \n",
       "max    1500.000000  32000.000000  64000.000000  256.000000   52.000000   \n",
       "\n",
       "            CHMAX  \n",
       "count  167.000000  \n",
       "mean    18.616766  \n",
       "std     27.489919  \n",
       "min      0.000000  \n",
       "25%      5.000000  \n",
       "50%      8.000000  \n",
       "75%     24.000000  \n",
       "max    176.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking summary stats\n",
    "\n",
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0e4fb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MYCT     0\n",
       "MMIN     0\n",
       "MMAX     0\n",
       "CACH     0\n",
       "CHMIN    0\n",
       "CHMAX    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing values\n",
    "\n",
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4977a1a1",
   "metadata": {},
   "source": [
    "We don't have any missing values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6733727",
   "metadata": {},
   "source": [
    "## 4 - Data Preprocessing \n",
    "\n",
    "It is here that we prepare the data to be in the proper format for the machine learning model. \n",
    "\n",
    "Decision trees don't care if the features are scaled or not. Let's set up a pipeline to scale features, and we will use it to verify that notion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7ee6d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "scale_pipe = Pipeline([\n",
    "    ('scaler', StandardScaler())\n",
    "    \n",
    "])\n",
    "\n",
    "X_train_scaled = scale_pipe.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd496de6",
   "metadata": {},
   "source": [
    "## 5 - Training Decision Tree Regressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8311b87a",
   "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>DecisionTreeRegressor()</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\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "\n",
    "tree_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757c0a01",
   "metadata": {},
   "source": [
    "Let's train the same model on the scaled data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea28474e",
   "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>DecisionTreeRegressor()</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\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg_scaled = DecisionTreeRegressor()\n",
    "\n",
    "tree_reg_scaled.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afb9bde",
   "metadata": {},
   "source": [
    "## 6 - Evaluating Decision Trees\n",
    "\n",
    "Let's first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven't improved it yet. I will make a function to make it easier and to avoid repetitions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6962359e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def predict(input_data,model,labels):\n",
    "    \"\"\"\n",
    "    Take the input data, model and labels and return predictions\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    preds = model.predict(input_data)\n",
    "    mse = mean_squared_error(labels,preds)\n",
    "    rmse = np.sqrt(mse)\n",
    "    rmse\n",
    "    \n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a17fed16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.724590719956222"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train, tree_reg, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "109b17a0",
   "metadata": {},
   "source": [
    "And when the training data is scaled..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c943c75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.724590719956222"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train_scaled, tree_reg_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a78d94",
   "metadata": {},
   "source": [
    "As you can see, there is no difference at all. So in your future projects using decision trees, whether you scale the data or not, your predictions will not be affected. \n",
    "\n",
    "But don't do this...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d2b92b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201.6303904952264"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train_scaled, tree_reg, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9fd8d8",
   "metadata": {},
   "source": [
    "If you can inspect the parameters that I passed in predict function above, there are scaled data, model trained on unscaled data, and labels(y_train). As you may have guessed, that is a complete mismatch which led to poor results. If you trained a model on scaled data, the data that you predict the model on should be scaled in the same way too. \n",
    "\n",
    "Let's try to improve the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a574a5",
   "metadata": {},
   "source": [
    "## 7 - Improving Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f92566d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'squared_error',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af110f75",
   "metadata": {},
   "source": [
    "We have seen that one way to improve the decision tree model is to find the right number of max_depth and some other few parameters. Let's use GridSearch to find best hyperparameters. Note that this may end up overfitting because we have a small dataset. But since we are doing this for learning purpose (you can adapt it to your problem), let's do it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "056818ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 250 candidates, totalling 750 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeRegressor(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 0, 1, 2, 3],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "                         &#x27;min_samples_split&#x27;: [0, 1, 2, 3, 4]},\n",
       "             verbose=1)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeRegressor(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 0, 1, 2, 3],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "                         &#x27;min_samples_split&#x27;: [0, 1, 2, 3, 4]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=DecisionTreeRegressor(random_state=42),\n",
       "             param_grid={'max_depth': [None, 0, 1, 2, 3],\n",
       "                         'max_leaf_nodes': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "                         'min_samples_split': [0, 1, 2, 3, 4]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params_grid = {'max_leaf_nodes': list(range(0, 10)), 'min_samples_split': [0,1,2, 3, 4], \n",
    "              'max_depth':[None,0,1,2,3]}\n",
    "\n",
    "#refit is true by default. The best estimator is trained on the whole dataset \n",
    "\n",
    "grid_search = GridSearchCV(DecisionTreeRegressor(random_state=42), params_grid, verbose=1, cv=3, refit=True)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55a5d7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': None, 'max_leaf_nodes': 9, 'min_samples_split': 4}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c4a1488d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_leaf_nodes=9, min_samples_split=4, random_state=42)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_leaf_nodes=9, min_samples_split=4, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_leaf_nodes=9, min_samples_split=4, random_state=42)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4995ab2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fc64ef",
   "metadata": {},
   "source": [
    "Let's make prediction on the training data again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "85b1b211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.999530266023044"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_train, tree_best, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93b6002",
   "metadata": {},
   "source": [
    "Like we said, we overfitted. The RMSE on our small dataset was pretty enough (and you probably would not use decision trees for small data like this we are using). \n",
    "\n",
    "Finally we can evaluate the model on the test set. Let's use the orginal model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f03cc7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.10928802805393"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(X_test, tree_reg, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5ebfcd",
   "metadata": {},
   "source": [
    "Clearly, the model overfitted and the improvements we tried didn't improve anything. This is not an issue of the model or our approach to improve the model. Decision trees are complex models for the data we have. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af266a08",
   "metadata": {},
   "source": [
    "This is the end of the notebook. We have learned the fundamental idea behind the decision trees, and used it to predict the CPU performance. In the next lab, we will use it for classification task and we will use a real world dataset so that we can practically improve the decision trees model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1f5a3f",
   "metadata": {},
   "source": [
    "## Acknowledgments\n",
    "\n",
    "Thanks to Jake VanderPlas for creating the open-source course [ Python Data Science Handbook ](https://github.com/jakevdp/PythonDataScienceHandbook). It inspires the majority of the content in this chapter."
   ]
  }
 ],
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