k_fold_cv_article_guide.ipynb

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  "metadata": {
    "colab": {
      "name": "k_fold_cv_article_guide.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMKmRNvrhCiYG9wOvV5YLsq",
      "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/Inyrkz/breast_cancer/blob/main/k_fold_cv_article_guide.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "PA1li5j9EVgv",
        "outputId": "0a033c10-e303-42ea-fb49-a5aea8b5c83c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
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              "      <th>radius_se</th>\n",
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              "      <th>perimeter_se</th>\n",
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              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
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              "      <th>fractal_dimension_se</th>\n",
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              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
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              "      <td>184.60</td>\n",
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              "      <th>1</th>\n",
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              "      <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>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
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              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</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",
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              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
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              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
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              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
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              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\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",
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              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
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              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
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              "      <td>27.23</td>\n",
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              "      <td>0.05963</td>\n",
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              "      <td>14.910</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\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>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
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              "      <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",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\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>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\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>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\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>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\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>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\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>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</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>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0      842302         M  ...                  0.11890          NaN\n",
              "1      842517         M  ...                  0.08902          NaN\n",
              "2    84300903         M  ...                  0.08758          NaN\n",
              "3    84348301         M  ...                  0.17300          NaN\n",
              "4    84358402         M  ...                  0.07678          NaN\n",
              "..        ...       ...  ...                      ...          ...\n",
              "564    926424         M  ...                  0.07115          NaN\n",
              "565    926682         M  ...                  0.06637          NaN\n",
              "566    926954         M  ...                  0.07820          NaN\n",
              "567    927241         M  ...                  0.12400          NaN\n",
              "568     92751         B  ...                  0.07039          NaN\n",
              "\n",
              "[569 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# import relevant libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import f1_score\n",
        "%matplotlib inline\n",
        "\n",
        "# load dataset\n",
        "dataset = pd.read_csv('breast_cancer_data.csv')\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check dataset info\n",
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjHLAKWaFMCW",
        "outputId": "c6c86521-deb6-40d0-fae9-da72c022f6fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 33 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            "dtypes: float64(31), int64(1), object(1)\n",
            "memory usage: 146.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and target variable\n",
        "X = dataset.iloc[:, 2:-1].values\n",
        "y = dataset. iloc [:, 1].values\n",
        "print(\"Matrix of features\", X, sep='\\n')\n",
        "print(\"--------------------------------------------------\")\n",
        "print(\"Target Variable\", y, sep='\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aje5B1J2FhOF",
        "outputId": "fec97354-5bbb-4d0b-bbec-c9d0e298a198"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matrix of features\n",
            "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
            " ...\n",
            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n",
            "--------------------------------------------------\n",
            "Target Variable\n",
            "['M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M'\n",
            " 'M' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M'\n",
            " 'M' 'B' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M'\n",
            " 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B'\n",
            " 'M' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'M' 'M' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B'\n",
            " 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'B'\n",
            " 'M' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B'\n",
            " 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M'\n",
            " 'M' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B'\n",
            " 'M' 'M' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'M' 'M'\n",
            " 'M' 'M' 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'M' 'M' 'M'\n",
            " 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'M'\n",
            " 'B' 'B' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B'\n",
            " 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B'\n",
            " 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B'\n",
            " 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'M'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'M' 'M' 'B']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Label Encode the target variable\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "encoded_y = label_encoder.fit_transform(y)\n",
        "label_encoder_name_mapping = dict(zip(label_encoder.classes_,\n",
        "                                     label_encoder.transform(label_encoder.classes_)))\n",
        "print(\"Mapping of Label Encoded Classes\", label_encoder_name_mapping, sep=\"\\n\")\n",
        "print(\"Label Encoded Target Variable\", encoded_y, sep=\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s_6_qO5yFp49",
        "outputId": "70293660-14f5-42cd-c7fe-5b7213fab02e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mapping of Label Encoded Classes\n",
            "{'B': 0, 'M': 1}\n",
            "Label Encoded Target Variable\n",
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 1\n",
            " 0 1 0 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0\n",
            " 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0\n",
            " 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1\n",
            " 0 1 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0\n",
            " 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0\n",
            " 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1\n",
            " 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
            " 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0\n",
            " 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0\n",
            " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1\n",
            " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# K-Fold Cross-Validation\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "def cross_validation(model, _X, _y, _cv=5):\n",
        "  '''Function to perform 5 Folds Cross-Validation\n",
        "\n",
        "   Parameters\n",
        "   ----------\n",
        "  model : Python Class, default=None\n",
        "          This is the machine learning algorithm to be used for training.\n",
        "\n",
        "  _X : array\n",
        "       This is the matrix of features.\n",
        "\n",
        "  _y: array\n",
        "       This is the  target variable.\n",
        "\n",
        "  _cv : int, default=5\n",
        "      Determines the number of folds for cross-validation.\n",
        "\n",
        "   Returns\n",
        "   -------\n",
        "   Function returns a dictionary containing the metrics 'accuracy', 'precision',\n",
        "   'recall', 'f1' for both training set and validation set.\n",
        "  '''\n",
        "\n",
        "  _scoring = ['accuracy', 'precision', 'recall', 'f1']\n",
        "  results = cross_validate(estimator=model,\n",
        "                           X=_X,\n",
        "                           y=_y,\n",
        "                           cv=_cv,\n",
        "                           scoring=_scoring,\n",
        "                           return_train_score=True)\n",
        "  \n",
        "  return {\"Training Accuracy scores\": results['train_accuracy'],\n",
        "          \"Mean Training Accuracy\": results['train_accuracy'].mean()*100,\n",
        "          \"Training Precision scores\": results['train_precision'],\n",
        "          \"Mean Training Precision\": results['train_precision'].mean(),\n",
        "          \"Training Recall scores\": results['train_recall'],\n",
        "          \"Mean Training Recall\": results['train_recall'].mean(),\n",
        "          \"Training F1 scores\": results['train_f1'],\n",
        "          \"Mean Training F1 Score\": results['train_f1'].mean(),\n",
        "          \"Validation Accuracy scores\": results['test_accuracy'],\n",
        "          \"Mean Validation Accuracy\": results['test_accuracy'].mean()*100,\n",
        "          \"Validation Precision scores\": results['test_precision'],\n",
        "          \"Mean Validation Precision\": results['test_precision'].mean(),\n",
        "          \"Validation Recall scores\": results['test_recall'],\n",
        "          \"Mean Validation Recall\": results['test_recall'].mean(),\n",
        "          \"Validation F1 scores\": results['test_f1'],\n",
        "          \"Mean Validation F1 Score\": results['test_f1'].mean()\n",
        "          }"
      ],
      "metadata": {
        "id": "AeFcHUGYGHeH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Grouped Bar Chart for both training and validation data\n",
        "def plot_result(x_label, y_label, plot_title, train_data, val_data):\n",
        "    '''Function to plot a grouped bar chart showing the training and validation\n",
        "      results of the ML model in each fold after applying K-fold cross validation.\n",
        "\n",
        "     Parameters\n",
        "     ----------\n",
        "     x_label: str, \n",
        "        Name of the algorithm used for training e.g 'Decision Tree'\n",
        "      \n",
        "     y_label: str, \n",
        "        Name of metric being visualized e.g 'Accuracy'\n",
        "\n",
        "     plot_title: str, \n",
        "        This is the title of the plot e.g 'Accuracy Plot'\n",
        "     \n",
        "     train_result: list, array\n",
        "        This is the list containing either training precision, accuracy, or f1 score.\n",
        "    \n",
        "     val_result: list, array\n",
        "        This is the list containing either validation precision, accuracy, or f1 score.\n",
        "\n",
        "     Returns\n",
        "     -------\n",
        "     Function returns a Grouped Barchart showing the training and validation result\n",
        "     in each fold.\n",
        "    '''\n",
        "    \n",
        "    # Set size of plot\n",
        "    plt.figure(figsize=(12,6))\n",
        "    labels = [\"1st Fold\", \"2nd Fold\", \"3rd Fold\", \"4th Fold\", \"5th Fold\"]\n",
        "    X_axis = np.arange(len(labels))\n",
        "\n",
        "    ax = plt.gca()\n",
        "    plt.ylim(0.40000, 1)\n",
        "    plt.bar(X_axis-0.2, train_data, 0.4, color='blue', label='Training')\n",
        "    plt.bar(X_axis+0.2, val_data, 0.4, color='red', label='Validation')\n",
        "    plt.title(plot_title, fontsize=30)\n",
        "    plt.xticks(X_axis, labels)\n",
        "    plt.xlabel(x_label, fontsize=14)\n",
        "    plt.ylabel(y_label, fontsize=14)\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "hU9jo808Jkwn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "decision_tree_model = DecisionTreeClassifier(criterion=\"entropy\",\n",
        "                                             random_state=0)\n",
        "decision_tree_result = cross_validation(decision_tree_model, X, encoded_y, 5)\n",
        "print(decision_tree_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zB2LvW8nL_PO",
        "outputId": "7b4fbab8-91c4-480e-acc7-b88f7d6df6de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([1., 1., 1., 1., 1.]), 'Mean Training Accuracy': 100.0, 'Training Precision scores': array([1., 1., 1., 1., 1.]), 'Mean Training Precision': 1.0, 'Training Recall scores': array([1., 1., 1., 1., 1.]), 'Mean Training Recall': 1.0, 'Training F1 scores': array([1., 1., 1., 1., 1.]), 'Mean Training F1 Score': 1.0, 'Validation Accuracy scores': array([0.9122807 , 0.92105263, 0.94736842, 0.94736842, 0.94690265]), 'Mean Validation Accuracy': 93.49945660611706, 'Validation Precision scores': array([0.92307692, 0.94736842, 0.90909091, 0.89130435, 0.89130435]), 'Mean Validation Precision': 0.9124289897745275, 'Validation Recall scores': array([0.8372093 , 0.8372093 , 0.95238095, 0.97619048, 0.97619048]), 'Mean Validation Recall': 0.9158361018826134, 'Validation F1 scores': array([0.87804878, 0.88888889, 0.93023256, 0.93181818, 0.93181818]), 'Mean Validation F1 Score': 0.9121613182305184}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Accuracy Result\n",
        "model_name = \"Decision Tree\"\n",
        "plot_result(model_name,\n",
        "            \"Accuracy\",\n",
        "            \"Accuracy scores in 5 Folds\",\n",
        "            decision_tree_result[\"Training Accuracy scores\"],\n",
        "            decision_tree_result[\"Validation Accuracy scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "yb4TEyF1MUHX",
        "outputId": "b7df330a-5958-4792-aaaa-67592135aa17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Precision Result\n",
        "plot_result(model_name,\n",
        "            \"Precision\",\n",
        "            \"Precision scores in 5 Folds\",\n",
        "            decision_tree_result[\"Training Precision scores\"],\n",
        "            decision_tree_result[\"Validation Precision scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "tkbGmF6bMY2g",
        "outputId": "5e36ef4d-e3f0-4300-d74a-3f03ef64723e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Recall Result\n",
        "plot_result(model_name,\n",
        "            \"Recall\",\n",
        "            \"Recall scores in 5 Folds\",\n",
        "            decision_tree_result[\"Training Recall scores\"],\n",
        "            decision_tree_result[\"Validation Recall scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "3EC8aSkyMbKs",
        "outputId": "2aa54b77-d8bc-4d0d-d71c-69dfa931ffae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot F1-Score Result\n",
        "plot_result(model_name,\n",
        "            \"F1\",\n",
        "            \"F1 Scores in 5 Folds\",\n",
        "            decision_tree_result[\"Training F1 scores\"],\n",
        "            decision_tree_result[\"Validation F1 scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "FPFwNbImMcFB",
        "outputId": "8f18b62d-b6e2-4f43-a11e-1dca6a798918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree_model_2 = DecisionTreeClassifier(criterion=\"entropy\",\n",
        "                                               min_samples_split=5,\n",
        "                                               random_state=0)\n",
        "\n",
        "decision_tree_result_2 = cross_validation(decision_tree_model_2, X, encoded_y, 5)\n",
        "print(decision_tree_result_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pu0Ac2s8L2fg",
        "outputId": "7b7913da-9179-41a0-fe71-7d4814aa212a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.99340659, 0.99340659, 1.        , 0.9956044 , 0.99342105]), 'Mean Training Accuracy': 99.51677270098322, 'Training Precision scores': array([1.        , 1.        , 1.        , 1.        , 0.99408284]), 'Mean Training Precision': 0.9988165680473372, 'Training Recall scores': array([0.98224852, 0.98224852, 1.        , 0.98823529, 0.98823529]), 'Mean Training Recall': 0.9881935259310826, 'Training F1 scores': array([0.99104478, 0.99104478, 1.        , 0.99408284, 0.99115044]), 'Mean Training F1 Score': 0.9934645669906736, 'Validation Accuracy scores': array([0.92105263, 0.93859649, 0.94736842, 0.93859649, 0.94690265]), 'Mean Validation Accuracy': 93.85033379909953, 'Validation Precision scores': array([0.925     , 1.        , 0.90909091, 0.88888889, 0.89130435]), 'Mean Validation Precision': 0.9228568291611768, 'Validation Recall scores': array([0.86046512, 0.8372093 , 0.95238095, 0.95238095, 0.97619048]), 'Mean Validation Recall': 0.9157253599114064, 'Validation F1 scores': array([0.89156627, 0.91139241, 0.93023256, 0.91954023, 0.93181818]), 'Mean Validation F1 Score': 0.9169099279932611}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Accuracy Result\n",
        "plot_result(model_name,\n",
        "            \"Accuracy\",\n",
        "            \"Accuracy scores in 5 Folds\",\n",
        "            decision_tree_result_2[\"Training Accuracy scores\"],\n",
        "            decision_tree_result_2[\"Validation Accuracy scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "B8TbwpqiNBv2",
        "outputId": "bedbf3c6-bbb8-494d-dbb2-97a120c66e4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Precision Result\n",
        "plot_result(model_name,\n",
        "            \"Precision\",\n",
        "            \"Precision scores in 5 Folds\",\n",
        "            decision_tree_result_2[\"Training Precision scores\"],\n",
        "            decision_tree_result_2[\"Validation Precision scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "1nl9GlDLNBv9",
        "outputId": "de7082d0-692d-4955-91a5-c430f2ac0d71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Recall Result\n",
        "plot_result(model_name,\n",
        "            \"Recall\",\n",
        "            \"Recall scores in 5 Folds\",\n",
        "            decision_tree_result_2[\"Training Recall scores\"],\n",
        "            decision_tree_result_2[\"Validation Recall scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "0qgEPE3QNBv_",
        "outputId": "e522f490-4eeb-4742-980a-7b9825d86b5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot F1-Score Result\n",
        "plot_result(model_name,\n",
        "            \"F1\",\n",
        "            \"F1 Scores in 5 Folds\",\n",
        "            decision_tree_result_2[\"Training F1 scores\"],\n",
        "            decision_tree_result_2[\"Validation F1 scores\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "pwEZNEIxNBwC",
        "outputId": "2818d38f-ca41-4b01-953c-e44c7a416248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}