FMML-22-CLF1-Lab4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lakshmilikithabonthu/Fmml/blob/main/FMML-22-CLF1-Lab4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zx0DuwfhUoO"
      },
      "source": [
        "# **FOUNDATIONS OF MODERN MACHINE LEARNING, IIIT Hyderabad**\n",
        "### MODULE: CLASSIFICATION-1\n",
        "### LAB-4 : Understanding Cross-Validation and Standardization\n",
        "#### Module Coordinator: Tanvi Kamble\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Section 1: Tweaking the parameters**"
      ],
      "metadata": {
        "id": "qH_EEbUzvRBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's stat the lab by writing knn code on a cancer detection dataset\n"
      ],
      "metadata": {
        "id": "ZPj-s_67uscz"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITvv8HAmjquR"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import datasets"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cancer = datasets.load_breast_cancer(as_frame=True)"
      ],
      "metadata": {
        "id": "167jISBahmSu"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cancer.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoKfdTQrKGnM",
        "outputId": "2222e0e6-6643-4f39-fdca-dd4745c32fba"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cancer.frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "l1Fp6m9jhrpP",
        "outputId": "bccd782a-5b5f-4eeb-f566-78bc8c98e515"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                   0.07871  ...          17.33           184.60      2019.0   \n",
              "1                   0.05667  ...          23.41           158.80      1956.0   \n",
              "2                   0.05999  ...          25.53           152.50      1709.0   \n",
              "3                   0.09744  ...          26.50            98.87       567.7   \n",
              "4                   0.05883  ...          16.67           152.20      1575.0   \n",
              "..                      ...  ...            ...              ...         ...   \n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
              "0                  0.2654          0.4601                  0.11890       0  \n",
              "1                  0.1860          0.2750                  0.08902       0  \n",
              "2                  0.2430          0.3613                  0.08758       0  \n",
              "3                  0.2575          0.6638                  0.17300       0  \n",
              "4                  0.1625          0.2364                  0.07678       0  \n",
              "..                    ...             ...                      ...     ...  \n",
              "564                0.2216          0.2060                  0.07115       0  \n",
              "565                0.1628          0.2572                  0.06637       0  \n",
              "566                0.1418          0.2218                  0.07820       0  \n",
              "567                0.2650          0.4087                  0.12400       0  \n",
              "568                0.0000          0.2871                  0.07039       1  \n",
              "\n",
              "[569 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd2fe1f4-0105-4f89-8e56-1e0626d37e35\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd2fe1f4-0105-4f89-8e56-1e0626d37e35')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd2fe1f4-0105-4f89-8e56-1e0626d37e35 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cd2fe1f4-0105-4f89-8e56-1e0626d37e35');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P69_W-cnTF4"
      },
      "source": [
        "X = cancer.data\n",
        "y = cancer.target"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4SlDqmync4p"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 17)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 1.1: Changing Value of K\n",
        "Let's try to run the knn with different values of knn and compare the results"
      ],
      "metadata": {
        "id": "A987m6FMvaLm"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owajk896p9yc"
      },
      "source": [
        "neighbors = []\n",
        "scores_list = []\n",
        "  \n",
        "for k in range(1, 25):\n",
        "    neighbors.append(k)\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(X_train,y_train)\n",
        "    scores = knn.score(X_test,y_test)\n",
        "    scores_list.append(scores)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "UvD57XSyqSVT",
        "outputId": "957ec199-375a-4a81-e6f0-df0affbbb01a"
      },
      "source": [
        "Accuracy = [x*100 for x in scores_list]\n",
        "# plot Accuracy versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlim([0, 25])\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Accuracy in %')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_PArXY3phVM",
        "outputId": "288a1119-73ae-4837-b62e-b3e6d2a7ff8a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "93.61702127659575\n",
            "[10, 12, 13, 14, 22, 24]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this case, we see that the ideal number of neighbours can take any one of the values in the list [10,12,13,14,22,24].  \n",
        "However, this only tells us the effectiveness of the KNN classifier only if we have the same train-test split considered above. It still does not tell us how well the algorithm would perform if the test set were different. The results would have been different if we had split the data differently."
      ],
      "metadata": {
        "id": "AYObfxqdsGqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### AUC-ROC Curve"
      ],
      "metadata": {
        "id": "uD1tc30Bvnec"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The AUC - ROC curve is a performance measurement for classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability.\n",
        "It tells how much the model is capable of distinguishing between classes.  \n",
        "The diagonal in the curve divides the ROC space. Points above the diagonal represent good classification results (better than random); points below the line represent bad results (worse than random).    \n",
        "Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1.\n",
        "\n",
        "The ROC curve is plotted with the TPR (True Positive Rate) on the y-axis, and the FPR (False Positive Rate) on the x-axis."
      ],
      "metadata": {
        "id": "3US9wcfViqNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## AUC-ROC\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_roc(y_test,predicted):\n",
        "  y_scores = predicted\n",
        "  fpr, tpr, threshold = roc_curve(y_test, y_scores)\n",
        "  roc_auc = auc(fpr, tpr)\n",
        "  plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
        "  plt.legend(loc = 'lower right')\n",
        "  plt.plot([0, 1], [0, 1],'r--')\n",
        "  plt.xlim([0, 1])\n",
        "  plt.ylim([0, 1])\n",
        "  plt.ylabel('True Positive Rate')\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.title('Receiver Operating Characteristic')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "SAp-lfCCiced"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 10)\n",
        "knn.fit(X_train,y_train)\n",
        "predictions = knn.predict(X_test)\n",
        "plot_roc(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jcNAfOc6ihrv",
        "outputId": "4d45f314-321d-4f47-edfe-4c6cc61dc14f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 1.2: KNN using a different train-test split"
      ],
      "metadata": {
        "id": "CO7rXVXkiwsL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Changing the random_state value here so that data is split differently\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 1089)"
      ],
      "metadata": {
        "id": "v-i6n6y3swm4"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neighbors = []\n",
        "scores_list = []\n",
        "  \n",
        "for k in range(1, 25):\n",
        "    neighbors.append(k)\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(X_train,y_train)\n",
        "    scores = knn.score(X_test,y_test)\n",
        "    scores_list.append(scores)"
      ],
      "metadata": {
        "id": "yUQsEfAjsFaS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy = [x*100 for x in scores_list]\n",
        "# plot Accuracy versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlim([0, 25])\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "wy9EH2Yms5e_",
        "outputId": "d349f28a-858f-4030-8edd-f9ed70d00aad"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEEUOUOfs7_E",
        "outputId": "ad8c69c7-33f6-4f83-f1f4-e7f18603112b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94.14893617021278\n",
            "[5, 7, 8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, the model gives different results when we split the data differently. Here, it suggests that the ideal k value is 5, 7 or 8."
      ],
      "metadata": {
        "id": "l-yZCIU8tdo2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Section 2: Cross Validation**"
      ],
      "metadata": {
        "id": "0GOXxe-Wv_0w"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pfnhgihivrn3"
      },
      "source": [
        "As you saw in the above two cases with different train-test splits, there is a risk of overfitting on the test set because the parameters can be tweaked until the estimator performs optimally. This way, knowledge about the test set can “leak” into the model and evaluation metrics no longer report on generalization performance.  \n",
        "\n",
        "Cross Validation is an important technique in Machine Learning to analyze the performance of a model on **unseen** data.  \n",
        "\n",
        "There are different kinds of cross-validation methods: exhaustive and non-exhaustive.   \n",
        "Exhaustive cross-validation methods are cross-validation methods which learn and test on all possible ways to divide the original sample into a training and a validation set.\n",
        "Non-exhaustive cross validation methods do not compute all ways of splitting the original sample.\n",
        "\n",
        "One of the most commonly cross-validation methods is **K-Fold Cross validation**. The procedure for K-Fold Cross Validation is as follows:  \n",
        "1. Split the dataset into k groups (typically without shuffling)\n",
        "2. For each unique group:  \n",
        "  a. Take the group as a hold out or test data set  \n",
        "  b. Take the remaining groups as a training data set  \n",
        "  c. Fit a model on the training set and evaluate it on the test set  \n",
        "  d. Retain the evaluation score and discard the model  \n",
        "3. Summarize the performance of the model using the mean of model evaluation scores over all the folds."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Image source: https://scikit-learn.org/stable/modules/cross_validation.html  \n",
        "![Screenshot from 2021-12-13 09-37-46.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "aKUDUmiS-g06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 17)"
      ],
      "metadata": {
        "id": "9xjLYb1ZvaqM"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from statistics import mean\n",
        "kf = KFold(n_splits=10)\n",
        "cv_scores = []\n",
        "neighbors = []\n",
        "for k in range(1,25):\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  neighbors.append(k)\n",
        "  acclist = []\n",
        "  for train_index, test_index in kf.split(X_train, y_train):\n",
        "    x_train_fold, x_test_fold = X_train.iloc[train_index], X_train.iloc[test_index]\n",
        "    y_train_fold, y_test_fold = y_train.iloc[train_index], y_train.iloc[test_index]\n",
        "    knn.fit(x_train_fold, y_train_fold)\n",
        "    acclist.append(knn.score(x_test_fold, y_test_fold))\n",
        "  cv_scores.append(mean(acclist))"
      ],
      "metadata": {
        "id": "RB0Te1S8uiT6"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "UjpQTP7QpokH",
        "outputId": "54d02c75-e61f-4ca6-f1a5-af1b9ec03d5c"
      },
      "source": [
        "Accuracy = [x*100 for x in cv_scores]\n",
        "  \n",
        "# plot misclassification error versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Mean Cross-validation accuracy in %')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-XBDufnxbuT",
        "outputId": "da5791ab-3cb4-4dea-810b-7dd25bc59858"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10, 12]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that the accuracy in the above case is not the accuracy on the test set. We plot the mean of the accuracies obtained during cross-validation, when we considered different splits of the training set.  \n",
        "\n",
        "From the plot above, we can see that when the value of k is 10 or 12, our KNN model generalizes well to unseen data. Performing such cross validation gives us an idea about the parameters to choose when using a machine-learning algorithm."
      ],
      "metadata": {
        "id": "AHbBLzXWxzsh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## TASK-1:\n",
        "## In the above case, we saw K-Fold cross validation, where we split the dataset into K consecutive folds without taking anything else into account.\n",
        "## Stratified KFold is another strategy that is commonly used, which preserves the percentage of samples for each class in the folds.\n",
        "## Implement Stratified KFold (refer Sklearn docs) and display the results you obtain"
      ],
      "metadata": {
        "id": "ulg8ESJ77bIz"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TASK 1 ANSWER :"
      ],
      "metadata": {
        "id": "PeiNipD1lf_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.27, random_state = 20)"
      ],
      "metadata": {
        "id": "WZpANm3ZbdOz"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from statistics import mean\n",
        "kf = KFold(n_splits=10)\n",
        "new_scores = []\n",
        "neighbors = []\n",
        "for k in range(1,25):\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  neighbors.append(k)\n",
        "  acclist = []\n",
        "  for train_index, test_index in kf.split(X_train, y_train):\n",
        "    x_train_fold, x_test_fold = X_train.iloc[train_index], X_train.iloc[test_index]\n",
        "    y_train_fold, y_test_fold = y_train.iloc[train_index], y_train.iloc[test_index]\n",
        "    knn.fit(x_train_fold, y_train_fold)\n",
        "    acclist.append(knn.score(x_test_fold, y_test_fold))\n",
        "  new_scores.append(mean(acclist))"
      ],
      "metadata": {
        "id": "ASxAkLY0krAT"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy = [x*100 for x in new_scores]\n",
        "  \n",
        "# plot misclassification error versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Mean Cross-validation accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Gt6J3ex7lKkL",
        "outputId": "7c272963-70e3-478f-dc4f-ab7e16d669c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "id": "_9rYFA-4lTNS",
        "outputId": "57b01f5d-af61-4306-8f0e-269ca9a14e72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94.22183507549362\n",
            "[10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Section 3: Standardization**\n"
      ],
      "metadata": {
        "id": "yyJCZFTxwIlp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling the data that we have available is a recommended pre-processing step when working with many machine learning algorithms. The scale and distribution of the data drawn from the domain may be different for each variable. Differences in the scales across input variables may increase the difficulty of the problem being modeled, and the model may suffer from poor performance during learning and sensitivity to input values resulting in higher generalization error.  \n",
        "\n",
        "Standardization (also called z-score normalization) is one such scaling technique that transforms your data such that the resulting distribution has a mean of 0 and a standard deviation of 1. \n",
        "\n",
        "$$ \\text{New (scaled) value} = {X - \\mu \\over \\sigma}$$ \n"
      ],
      "metadata": {
        "id": "LiST-3D1zVgA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = cancer.data\n",
        "y = cancer.target"
      ],
      "metadata": {
        "id": "GNe4-JTI0tYh"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "dwS4wRtFzUK_"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.33, random_state = 17)"
      ],
      "metadata": {
        "id": "rVg1mxfc0HgB"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "neighbors = []\n",
        "scores_list = []\n",
        "  \n",
        "for k in range(1, 25):\n",
        "    neighbors.append(k)\n",
        "    knn = KNeighborsClassifier(n_neighbors = k)\n",
        "    knn.fit(X_train,y_train)\n",
        "    scores = knn.score(X_test,y_test)\n",
        "    scores_list.append(scores)"
      ],
      "metadata": {
        "id": "yq6yq6FjzmcR"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy = [x*100 for x in scores_list]\n",
        "# plot Accuracy versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlim([0, 25])\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "ID5KNvw5zyOb",
        "outputId": "b19ef12a-cdc3-44e3-901c-6dfb7cb04f82"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determining the best k\n",
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tm1WNW1z0I7",
        "outputId": "d3fa6b31-abe5-41e7-97ad-3b472889918d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "96.80851063829788\n",
            "[2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## TASK-2:\n",
        "## Plot a confusion matrix for the classification above on the scaled data (using the optimal value of k)\n",
        "## Also print out the Precision, Recall and F1-score values"
      ],
      "metadata": {
        "id": "8m5kl4pU6oV7"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANSWER FOR TASK 2"
      ],
      "metadata": {
        "id": "b7p3OMeMlmmU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier  \n",
        "classifier= KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2 )  \n",
        "classifier.fit(X_train, y_train)  "
      ],
      "metadata": {
        "id": "FNzKl5XacF_8",
        "outputId": "2a936067-8bf8-4b37-affe-8a2f82f68622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "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>KNeighborsClassifier()</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the test set result  \n",
        "y_pred= classifier.predict(X_test)  "
      ],
      "metadata": {
        "id": "2QGPz37dextz"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating the Confusion matrix  \n",
        "from sklearn.metrics import confusion_matrix  \n",
        "cm= confusion_matrix(y_test, y_pred)  \n",
        "print(cm)"
      ],
      "metadata": {
        "id": "G3yzx_x3e34y",
        "outputId": "6d973fcc-84ba-492e-da0c-d2be9126c42e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 60   7]\n",
            " [  1 120]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TN=60\n",
        "FP=7\n",
        "FN=1\n",
        "TP=120\n",
        "precision=TP/TP+FP\n",
        "Recall=TP/TP+FN\n",
        "F1=(2*Recall*precision)/Recall+precision\n",
        "print(precision)#precision\n",
        "print(Recall)#Recall\n",
        "print(F1)#F1-score\n"
      ],
      "metadata": {
        "id": "sUJsiydZgegT",
        "outputId": "0b08e342-0a55-4259-c7e0-61a1162f4bac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8.0\n",
            "2.0\n",
            "24.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## TASK-3:\n",
        "## Perform K-Fold Cross validation after standardizing the data. Display your results."
      ],
      "metadata": {
        "id": "NIiLVOEPJFLN"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANSWER FOR TASK 3"
      ],
      "metadata": {
        "id": "ojLOb30blpKF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import KFold\n"
      ],
      "metadata": {
        "id": "-yjkYqWmjHRN"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy = [x*100 for x in scores_list]\n",
        "  \n",
        "# plot misclassification error versus k\n",
        "plt.figure(figsize = (10, 6))\n",
        "plt.plot(neighbors, Accuracy)\n",
        "plt.xlabel('Number of neighbors')\n",
        "plt.ylabel('Mean Cross-validation accuracy in %')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "uJCmJLwVjsH8",
        "outputId": "9659d7e1-f0e7-4bd7-8956-c66b3e73c271",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_acc = max(Accuracy)\n",
        "optimal_ks = [neighbors[i] for i, j in enumerate(Accuracy) if j == max_acc]\n",
        "print(max_acc)\n",
        "print(optimal_ks)"
      ],
      "metadata": {
        "id": "T9_tVazpkDak",
        "outputId": "a9d8ea20-6ed8-40b1-dba1-6ebc70d12b27",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "96.80851063829788\n",
            "[2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questions to Think About and Answer\n",
        "1. Instead of using cross-validation, we could also split our dataset into three sets- train, validation, and test set. The training proceeds on the training set, after which evaluation is done on the validation set, and when the experiment seems to be successful, final evaluation can be done on the test set.  \n",
        "What do you think are the pros/cons of this method ?  \n",
        "2. Is K-Fold cross validation an exhaustive or non-exhaustive cross validation method ? Justify your answer."
      ],
      "metadata": {
        "id": "W0HPRG2m_XAN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# ANSWERS"
      ],
      "metadata": {
        "id": "qJugAx2wltub"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1)** Instead of using cross-validation, we could also split our dataset into three sets- train, validation, and test set. The training proceeds on the training set, after which evaluation is done on the validation set, and when the experiment seems to be successful, final evaluation can be done on the test set.\n",
        "What do you think are the pros/cons of this method ?\n",
        "\n",
        "ANS : \n",
        "\n",
        "**PROS of Cross Validation**\n",
        "\n",
        "* Reduces Overfitting: In Cross Validation, we split the dataset into multiple folds and train the algorithm on different folds. This prevents our model from overfitting the training dataset. So, in this way, the model attains the generalization capabilities which is a good sign of a robust algorithm.\n",
        "\n",
        "* Note: Chances of overfitting are less if the dataset is large. So, Cross Validation may not be required at all in the situation where we have sufficient data available.\n",
        "\n",
        "* Hyperparameter Tuning: Cross Validation helps in finding the optimal value of hyperparameters to increase the efficiency of the algorithm.\n",
        "\n",
        "\n",
        "**CONS of Cross Validation**\n",
        "\n",
        "* Increases Training Time: Cross Validation drastically increases the training time. Earlier you had to train your model only on one training set, but with Cross Validation you have to train your model on multiple training sets. \n",
        "\n",
        "* For example, if you go with 5 Fold Cross Validation, you need to do 5 rounds of training each on different 4/5 of available data. And this is for only one choice of hyperparameters. If you have multiple choice of parameters, then the training period will shoot too high.\n",
        "\n",
        "* Needs Expensive Computation: Cross Validation is computationally very expensive in terms of processing power required."
      ],
      "metadata": {
        "id": "C6t04txSlv0L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#ANSWER2"
      ],
      "metadata": {
        "id": "PLklxHPWmZR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2)** Is K-Fold cross validation an exhaustive or non-exhaustive cross validation method ? Justify your answer.\n",
        "\n",
        "ANS:\n",
        "\n",
        "* Non-exhaustive cross-validation\n",
        "* K-fold cross validation where k is not equal to N, Stratified cross validation and repeated random sub-sampling validation are non-exhaustive cross validation methods."
      ],
      "metadata": {
        "id": "cQrYQU2Mmb9N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "3ERDfU-roxkM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Useful Resources for further reading\n",
        "1. Cross validation (scikit-learn): https://scikit-learn.org/stable/modules/cross_validation.html  \n",
        "2. Wikipedia: https://en.wikipedia.org/wiki/Cross-validation_(statistics)  \n",
        "3. Standardization: https://machinelearningmastery.com/standardscaler-and-minmaxscaler-transforms-in-python/  \n",
        "4. AUC-ROC : https://en.wikipedia.org/wiki/Receiver_operating_characteristic\n"
      ],
      "metadata": {
        "id": "p2VvIoDbB15O"
      }
    }
  ]
}