mod3_lab4.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/Harsha-070/FMML_IIITH/blob/main/mod3_lab4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zx0DuwfhUoO"
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      "source": [
        "# **FOUNDATIONS OF MODERN MACHINE LEARNING, IIIT Hyderabad**\n",
        "### MODULE: CLASSIFICATION-1\n",
        "### LAB-4 : Understanding Cross-Validation and Standardization\n",
        "#### Module Coordinator: Jashn Arora\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": "e01e69e8-9f28-4c4c-c09b-a603fed2991a"
      },
      "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": {
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          "height": 478
        },
        "id": "l1Fp6m9jhrpP",
        "outputId": "cb27ffef-482e-4cf4-92a3-e0ed9f881cb3"
      },
      "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]"
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              "  <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",
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              "    <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",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <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",
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              "    <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",
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              "    <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",
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              "    <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",
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              "      <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",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c8f37715-a387-46b3-a9bc-1f0bc03fe633 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-c8f37715-a387-46b3-a9bc-1f0bc03fe633');\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",
              "\n",
              "\n",
              "<div id=\"df-eaca192e-6522-4bc5-8b3c-9ea535852916\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eaca192e-6522-4bc5-8b3c-9ea535852916')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eaca192e-6522-4bc5-8b3c-9ea535852916 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\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": 542
        },
        "id": "UvD57XSyqSVT",
        "outputId": "78e66db6-252d-4d0d-d75a-06e7d74f91b9"
      },
      "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 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "5716cd88-e386-479a-ca07-19cce91e9b1c"
      },
      "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": 472
        },
        "id": "jcNAfOc6ihrv",
        "outputId": "7e258d05-fa02-4d5f-b4c2-539ff5e1339b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": 542
        },
        "id": "wy9EH2Yms5e_",
        "outputId": "65cb04c1-5558-422c-a104-43588f4d82c3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "4dfe2269-b053-403a-ef45-47c2f680db55"
      },
      "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 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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": 542
        },
        "id": "UjpQTP7QpokH",
        "outputId": "874693e4-41ef-4997-9def-a86322d2a23c"
      },
      "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 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "3f3ff509-4c37-4ef4-d637-0d1e9f70bc99"
      },
      "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": [
        "# **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": 21,
      "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": 22,
      "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": 23,
      "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": 24,
      "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": 542
        },
        "id": "ID5KNvw5zyOb",
        "outputId": "5185b775-b73a-41f5-b4c5-8344c262f039"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "39c73265-7f75-4727-9d85-1c4e06282570"
      },
      "execution_count": 26,
      "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": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## TASK-3:\n",
        "## Perform K-Fold Cross validation after standardizing the data. Display your results."
      ],
      "metadata": {
        "id": "NIiLVOEPJFLN"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "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": [
        "#1.\n",
        "   Splitting the dataset into three sets (train, validation, and test sets) is a common alternative to cross-validation. This approach has its own set of pros and cons:\n",
        "\n",
        "**Pros:**\n",
        "\n",
        "1. **Simplicity:** The train-validation-test split is straightforward and easy to implement. It requires less computational resources compared to cross-validation, making it a practical choice for large datasets or complex models.\n",
        "\n",
        "2. **Computational Efficiency:** Training the model only on the training set and evaluating it on the validation set reduces the computational burden compared to cross-validation, where the model is trained and evaluated multiple times.\n",
        "\n",
        "3. **Model Interpretability:** Since the model is trained only once on the training set, it may be easier to interpret and understand. Cross-validation, with its multiple rounds of training and evaluation, can sometimes make it harder to pinpoint the exact reasons for model behavior.\n",
        "\n",
        "4. **Data Exclusivity:** The test set is kept completely separate until the final evaluation, preventing any information leakage from the test set into the training or validation phases.\n",
        "\n",
        "**Cons:**\n",
        "\n",
        "1. **Variance in Evaluation:** The choice of the validation set can introduce variability in the evaluation. If the validation set is not representative of the overall data distribution, the model may perform well on the validation set but poorly on new, unseen data.\n",
        "\n",
        "2. **Limited Data:** With a fixed split into training, validation, and test sets, you have less data for training your model compared to using k-fold cross-validation. This could be a limitation, especially when dealing with small datasets.\n",
        "\n",
        "3. **Sensitivity to Split:** The performance of the model might be sensitive to the specific random split of data into training, validation, and test sets. A different split might lead to different evaluation results.\n",
        "\n",
        "4. **Waste of Data:** In a small dataset scenario, having a separate test set means that a portion of your limited data is not used for training or fine-tuning the model, potentially limiting its performance.\n",
        "\n",
        "5. **No Cross-Validation Benefits:** Cross-validation provides a more robust estimate of model performance by averaging over multiple splits, which can be crucial for obtaining a more reliable assessment of how well the model generalizes to new data.\n",
        "\n",
        "The choice between cross-validation and a simple train-validation-test split depends on the specific characteristics of your dataset, the problem at hand, and the computational resources available. Cross-validation is generally preferred when you have a limited amount of data and want a more reliable estimate of model performance, but a train-validation-test split can be a practical choice in certain situations."
      ],
      "metadata": {
        "id": "vHNCGFQPI8pb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2.\n",
        "\n",
        "K-Fold cross-validation is considered an exhaustive cross-validation method. In K-Fold cross-validation, the dataset is divided into K subsets or folds, and the model is trained and evaluated K times, each time using a different fold as the validation set and the remaining data as the training set. The performance metrics from each iteration are then averaged to obtain a final performance estimate.\n",
        "\n",
        "Here's the justification for why K-Fold cross-validation is considered exhaustive:\n",
        "\n",
        "1. **Utilizes all Data:**\n",
        "\n",
        "    Each data point gets to be in the validation set exactly once and in the training set K-1 times. This ensures that the model has the opportunity to be trained on the entire dataset over the K iterations.\n",
        "\n",
        "2. **Robust Performance Estimation:**\n",
        "   \n",
        "   \n",
        "    By repeating the training and evaluation process K times with different subsets of data, K-Fold cross-validation provides a more robust estimate of the model's performance. It helps to reduce the impact of the specific random partitioning of the data into training and validation sets that may occur with a single train-validation-test split.\n",
        "\n",
        "3. **Reduces Variance:**\n",
        "   \n",
        "    The averaging of performance metrics over K folds helps to reduce the variance in the performance estimate compared to a single train-validation split. This is particularly important when dealing with limited data, as it provides a more reliable indication of how well the model is likely to generalize to unseen data.\n",
        "\n",
        "4. **Addresses Data Imbalance:**\n",
        "   \n",
        "   In situations where the dataset has class imbalance or other peculiarities, K-Fold cross-validation helps ensure that each fold represents the overall distribution of the data, reducing the risk of biased performance estimates.\n",
        "\n",
        "While K-Fold cross-validation is more computationally intensive compared to a single train-validation split, its exhaustiveness in utilizing the entire dataset for training and validation makes it a widely used and recommended method for performance estimation in machine learning."
      ],
      "metadata": {
        "id": "Jfq3Q54TJIc0"
      }
    },
    {
      "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"
      }
    }
  ]
}