Variable_Selection.ipynb

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        "# CS-6600 Assignment 7 - Variable Selection\n",
        "\n",
        "**Your Name**:\n",
        "\n",
        "*Weber State University*"
      ]
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      "source": [
        "For this assignment we're going to build and attempt to optimize regression models using some of the techniques we're learned so far in class."
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      "source": [
        "First, let's import our favorite libraries:"
      ]
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pylab as plt"
      ]
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      "source": [
        "There are also some libraries and functions we'll need for creating and testing our models:"
      ]
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      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import Ridge, RidgeCV, Lasso, LassoCV\n",
        "from sklearn.metrics import mean_squared_error"
      ]
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      "source": [
        "The dataset we'll use for this is the \"Hitters\" dataset (\"Hitters.csv\"), which is a dataset of Major League Baseball player statistics from 1986-1987. More info about the dataset can be found [here](https://www.kaggle.com/datasets/floser/hitters)."
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              "          Unnamed: 0  AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  \\\n",
              "0     -Andy Allanson    293    66      1    30   29     14      1     293   \n",
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              "3      -Andre Dawson    496   141     20    65   78     37     11    5628   \n",
              "4  -Andres Galarraga    321    87     10    39   42     30      2     396   \n",
              "\n",
              "   CHits  ...  CRuns  CRBI  CWalks  League Division PutOuts  Assists  Errors  \\\n",
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              "   Salary  NewLeague  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_hitters"
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          "metadata": {},
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        }
      ],
      "source": [
        "url = 'https://drive.google.com/uc?export=download&id=11NGvo9A2eyHQe_t3u94zN_i0_C1op75U'\n",
        "df_hitters = pd.read_csv(url)\n",
        "df_hitters.head()"
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    {
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      "metadata": {
        "id": "25a48276-3c52-453a-a352-2fefb5cebdb0"
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      "source": [
        "Let's rename that first column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "a5856bab-4d16-48c0-9729-8b3d33349e4c",
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      "source": [
        "df_hitters.rename(columns={\"Unnamed: 0\": \"Player\"}, inplace=True) #Name the first column \"Player\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a8ae974-8b53-4f67-8f46-d85aa500b0de",
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      },
      "source": [
        "Note that the Salary variable is missing for some of the players. The isnull() function can be used to identify the missing observations. It returns a vector of the same length as the input vector, with a TRUE value for any elements that are missing, and a FALSE value for non-missing elements. The sum() function can then be used to count all of the missing elements:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "3baaab21-fad3-46ae-a099-d57716da256a",
      "metadata": {
        "id": "3baaab21-fad3-46ae-a099-d57716da256a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2af284e3-71c4-4b8a-9c54-8dd41067732d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of null values: 59\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of null values:\", df_hitters[\"Salary\"].isnull().sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b76e4987-8c5d-4e7d-8522-298a9b921fa4",
      "metadata": {
        "id": "b76e4987-8c5d-4e7d-8522-298a9b921fa4"
      },
      "source": [
        "We see that Salary is missing for 59 players. That's not too many. Let's drop them. The dropna() function removes all of the rows that have missing values in any variable:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "20214ecd-57c3-4e6e-a9f0-53ba4619e453",
      "metadata": {
        "id": "20214ecd-57c3-4e6e-a9f0-53ba4619e453",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcd140d6-af52-40e3-89a2-f1314ae58997"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensions of original data: (322, 21)\n",
            "Dimensions of modified data: (263, 21)\n",
            "Number of null values: 0\n"
          ]
        }
      ],
      "source": [
        "# Print the dimensions of the original Hitters data (322 rows x 20 columns)\n",
        "print(\"Dimensions of original data:\", df_hitters.shape)\n",
        "\n",
        "# Drop any rows the contain missing values, along with the player names\n",
        "df_hitters_clean = df_hitters.dropna()\n",
        "\n",
        "# Print the dimensions of the modified Hitters data (263 rows x 20 columns)\n",
        "print(\"Dimensions of modified data:\", df_hitters_clean.shape)\n",
        "\n",
        "# One last check: should return 0\n",
        "print(\"Number of null values:\", df_hitters_clean[\"Salary\"].isnull().sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63d2e923-3f3a-4542-8380-11cb0f41cc84",
      "metadata": {
        "id": "63d2e923-3f3a-4542-8380-11cb0f41cc84"
      },
      "source": [
        "Alright, so we've now dropped all the rows with missing data.\n",
        "\n",
        "Some of our predictors are categorical, so for this exercise we'll eliminate those. We'll also specify the variable, \"Salary\", that we're trying to predict."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "6161a472-b30a-4773-bc21-bc343e399319",
      "metadata": {
        "id": "6161a472-b30a-4773-bc21-bc343e399319",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "1f630eae-28ab-456b-9510-eddc8fafbfe6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  \\\n",
              "1      315    81      7    24   38     39     14    3449    835      69   \n",
              "2      479   130     18    66   72     76      3    1624    457      63   \n",
              "3      496   141     20    65   78     37     11    5628   1575     225   \n",
              "4      321    87     10    39   42     30      2     396    101      12   \n",
              "5      594   169      4    74   51     35     11    4408   1133      19   \n",
              "..     ...   ...    ...   ...  ...    ...    ...     ...    ...     ...   \n",
              "317    497   127      7    65   48     37      5    2703    806      32   \n",
              "318    492   136      5    76   50     94     12    5511   1511      39   \n",
              "319    475   126      3    61   43     52      6    1700    433       7   \n",
              "320    573   144      9    85   60     78      8    3198    857      97   \n",
              "321    631   170      9    77   44     31     11    4908   1457      30   \n",
              "\n",
              "     CRuns  CRBI  CWalks  PutOuts  Assists  Errors  \n",
              "1      321   414     375      632       43      10  \n",
              "2      224   266     263      880       82      14  \n",
              "3      828   838     354      200       11       3  \n",
              "4       48    46      33      805       40       4  \n",
              "5      501   336     194      282      421      25  \n",
              "..     ...   ...     ...      ...      ...     ...  \n",
              "317    379   311     138      325        9       3  \n",
              "318    897   451     875      313      381      20  \n",
              "319    217    93     146       37      113       7  \n",
              "320    470   420     332     1314      131      12  \n",
              "321    775   357     249      408        4       3  \n",
              "\n",
              "[263 rows x 16 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AtBat</th>\n",
              "      <th>Hits</th>\n",
              "      <th>HmRun</th>\n",
              "      <th>Runs</th>\n",
              "      <th>RBI</th>\n",
              "      <th>Walks</th>\n",
              "      <th>Years</th>\n",
              "      <th>CAtBat</th>\n",
              "      <th>CHits</th>\n",
              "      <th>CHmRun</th>\n",
              "      <th>CRuns</th>\n",
              "      <th>CRBI</th>\n",
              "      <th>CWalks</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>315</td>\n",
              "      <td>81</td>\n",
              "      <td>7</td>\n",
              "      <td>24</td>\n",
              "      <td>38</td>\n",
              "      <td>39</td>\n",
              "      <td>14</td>\n",
              "      <td>3449</td>\n",
              "      <td>835</td>\n",
              "      <td>69</td>\n",
              "      <td>321</td>\n",
              "      <td>414</td>\n",
              "      <td>375</td>\n",
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              "      <td>66</td>\n",
              "      <td>72</td>\n",
              "      <td>76</td>\n",
              "      <td>3</td>\n",
              "      <td>1624</td>\n",
              "      <td>457</td>\n",
              "      <td>63</td>\n",
              "      <td>224</td>\n",
              "      <td>266</td>\n",
              "      <td>263</td>\n",
              "      <td>880</td>\n",
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              "      <th>3</th>\n",
              "      <td>496</td>\n",
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              "      <td>20</td>\n",
              "      <td>65</td>\n",
              "      <td>78</td>\n",
              "      <td>37</td>\n",
              "      <td>11</td>\n",
              "      <td>5628</td>\n",
              "      <td>1575</td>\n",
              "      <td>225</td>\n",
              "      <td>828</td>\n",
              "      <td>838</td>\n",
              "      <td>354</td>\n",
              "      <td>200</td>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>321</td>\n",
              "      <td>87</td>\n",
              "      <td>10</td>\n",
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              "      <td>42</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>396</td>\n",
              "      <td>101</td>\n",
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              "      <td>48</td>\n",
              "      <td>46</td>\n",
              "      <td>33</td>\n",
              "      <td>805</td>\n",
              "      <td>40</td>\n",
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              "      <td>169</td>\n",
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              "      <td>51</td>\n",
              "      <td>35</td>\n",
              "      <td>11</td>\n",
              "      <td>4408</td>\n",
              "      <td>1133</td>\n",
              "      <td>19</td>\n",
              "      <td>501</td>\n",
              "      <td>336</td>\n",
              "      <td>194</td>\n",
              "      <td>282</td>\n",
              "      <td>421</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>497</td>\n",
              "      <td>127</td>\n",
              "      <td>7</td>\n",
              "      <td>65</td>\n",
              "      <td>48</td>\n",
              "      <td>37</td>\n",
              "      <td>5</td>\n",
              "      <td>2703</td>\n",
              "      <td>806</td>\n",
              "      <td>32</td>\n",
              "      <td>379</td>\n",
              "      <td>311</td>\n",
              "      <td>138</td>\n",
              "      <td>325</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>492</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "      <td>76</td>\n",
              "      <td>50</td>\n",
              "      <td>94</td>\n",
              "      <td>12</td>\n",
              "      <td>5511</td>\n",
              "      <td>1511</td>\n",
              "      <td>39</td>\n",
              "      <td>897</td>\n",
              "      <td>451</td>\n",
              "      <td>875</td>\n",
              "      <td>313</td>\n",
              "      <td>381</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>475</td>\n",
              "      <td>126</td>\n",
              "      <td>3</td>\n",
              "      <td>61</td>\n",
              "      <td>43</td>\n",
              "      <td>52</td>\n",
              "      <td>6</td>\n",
              "      <td>1700</td>\n",
              "      <td>433</td>\n",
              "      <td>7</td>\n",
              "      <td>217</td>\n",
              "      <td>93</td>\n",
              "      <td>146</td>\n",
              "      <td>37</td>\n",
              "      <td>113</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>573</td>\n",
              "      <td>144</td>\n",
              "      <td>9</td>\n",
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              "      <td>60</td>\n",
              "      <td>78</td>\n",
              "      <td>8</td>\n",
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              "      <td>857</td>\n",
              "      <td>97</td>\n",
              "      <td>470</td>\n",
              "      <td>420</td>\n",
              "      <td>332</td>\n",
              "      <td>1314</td>\n",
              "      <td>131</td>\n",
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              "      <th>321</th>\n",
              "      <td>631</td>\n",
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              "      <td>4908</td>\n",
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              "      <td>30</td>\n",
              "      <td>775</td>\n",
              "      <td>357</td>\n",
              "      <td>249</td>\n",
              "      <td>408</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>263 rows × 16 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-e7e44db0-9960-4288-8723-3d189f8fecc4');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f8b2b4e6-61c5-41a3-8497-3b33e5c1c03e\">\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 263,\n  \"fields\": [\n    {\n      \"column\": \"AtBat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147,\n        \"min\": 19,\n        \"max\": 687,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          268,\n          523,\n          327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 1,\n        \"max\": 238,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          115,\n          131,\n          163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HmRun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 40,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          27,\n          16,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Runs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 0,\n        \"max\": 130,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          78,\n          90,\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RBI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 0,\n        \"max\": 121,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          94,\n          95,\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Walks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 0,\n        \"max\": 105,\n        \"num_unique_values\": 87,\n        \"samples\": [\n          44,\n          39,\n          84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 24,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          14,\n          16,\n          17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAtBat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2286,\n        \"min\": 19,\n        \"max\": 14053,\n        \"num_unique_values\": 257,\n        \"samples\": [\n          209,\n          1966,\n          6063\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648,\n        \"min\": 4,\n        \"max\": 4256,\n        \"num_unique_values\": 241,\n        \"samples\": [\n          633,\n          108,\n          1145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHmRun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82,\n        \"min\": 0,\n        \"max\": 548,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          181,\n          9,\n          67\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CRuns\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 331,\n        \"min\": 2,\n        \"max\": 2165,\n        \"num_unique_values\": 226,\n        \"samples\": [\n          702,\n          128,\n          310\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CRBI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 323,\n        \"min\": 3,\n        \"max\": 1659,\n        \"num_unique_values\": 226,\n        \"samples\": [\n          504,\n          273,\n          579\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CWalks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 264,\n        \"min\": 1,\n        \"max\": 1566,\n        \"num_unique_values\": 207,\n        \"samples\": [\n          403,\n          39,\n          709\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PutOuts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 279,\n        \"min\": 0,\n        \"max\": 1377,\n        \"num_unique_values\": 199,\n        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            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#Create the response variable.\n",
        "y = df_hitters_clean.Salary\n",
        "\n",
        "# Drop the column with the independent variable (Salary), and the columns with categorical data\n",
        "#The axis=1 parameter specifies we're dropping columns, and not rows.\n",
        "X = df_hitters_clean.drop(['Salary', 'League', 'Division', 'NewLeague', 'Player'], axis=1)\n",
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0cb40ce1-458c-468a-98a3-6d0c421c5b41",
      "metadata": {
        "id": "0cb40ce1-458c-468a-98a3-6d0c421c5b41"
      },
      "source": [
        "Now, for this assignment we're going to create a subset that will be our training data, and a subset that will be our test data. Let's do an 80/20 split:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "faf91e60-0f54-43de-a5e2-f2760c5743cf",
      "metadata": {
        "id": "faf91e60-0f54-43de-a5e2-f2760c5743cf"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2,random_state = 6600)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ea6e517a-1c97-4a49-a236-9f6e9817a6c3",
      "metadata": {
        "id": "ea6e517a-1c97-4a49-a236-9f6e9817a6c3"
      },
      "source": [
        "## Backward Subset Selection ##"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9dc46611-f740-4727-949d-a2fb4050a393",
      "metadata": {
        "id": "9dc46611-f740-4727-949d-a2fb4050a393"
      },
      "source": [
        "If we build a linear model with all these inputs, we'll likely overfit the model. To avoid this, we'd like to build a model using only the most important variables. But, which subset of variables is most important?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a2effdb-e7ea-4da3-b89a-44bfeb702500",
      "metadata": {
        "id": "9a2effdb-e7ea-4da3-b89a-44bfeb702500"
      },
      "source": [
        "In lecture 15, we learned three methods for selecting the best subset of variables for a linear model:\n",
        "\n",
        "* Best subset selection\n",
        "* Forward stepwise selection\n",
        "* Backward stepwised selection\n",
        "\n",
        "We went over algorithms for each of these, and implemented two (best subset and forward stepwise) as Python code. In this assignment, you'll implement backward stepwise selection."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be80135c-05c7-4c16-9144-1fcac6538536",
      "metadata": {
        "id": "be80135c-05c7-4c16-9144-1fcac6538536"
      },
      "source": [
        "The basic algorithm for backward stepwise selection is:\n",
        "\n",
        "_Algorithm_\n",
        "\n",
        "- Let $M_{p}$ denote the full model which contains all $p$ predictors.\n",
        "\n",
        "- For $k=p,p-1,...,1$\n",
        "    - Consider all $k$ models that contain all but one of the predictors in $M_{k}$, for a total of $k-1$ predictors.\n",
        "    - Pick the best among these $k$ models, and call in $M_{k-1}$. Here can define _best_ as having the smallest $RSS$ or highest $R^{2}$\n",
        "- Select the single best model among $M_{0},M_{1},...,M_{n}$ using $C_{p}$, $BIC$, adjusted $R^{2}$ or any other method.\n",
        "\n",
        "For this assignment, use the largest $R^{2}$ to determine the best model at each step, and select the overall best model using the adjusted $R^{2}$."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47c560ee-e964-4636-8f95-ba351fee86d7",
      "metadata": {
        "id": "47c560ee-e964-4636-8f95-ba351fee86d7"
      },
      "source": [
        "We'll start out with a list of all the predictors:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "d331a2ed-073b-49ea-aed8-77fa2286a4ab",
      "metadata": {
        "id": "d331a2ed-073b-49ea-aed8-77fa2286a4ab"
      },
      "outputs": [],
      "source": [
        "backwards_features = list(X.columns)\n",
        "p = len(backwards_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bfe5a75c-c970-4472-9601-4c0ab3d3e387",
      "metadata": {
        "id": "bfe5a75c-c970-4472-9601-4c0ab3d3e387"
      },
      "source": [
        "We'll also create two empty lists that you should fill:\n",
        "* *R2_list_backwards* - Fill this with the best $R^{2}$ value that you find at each step.\n",
        "* *features_list_backwards* - Fill this with the best set of features that you find at each step.\n",
        "* *R2_test_list_backwards* - Fill this with the performance of the model on the test data using the best set of features found at each step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "3820c0bd-80e8-4650-a78e-5d467bf62e02",
      "metadata": {
        "id": "3820c0bd-80e8-4650-a78e-5d467bf62e02"
      },
      "outputs": [],
      "source": [
        "R2_list_backwards, features_list_backwards, R2_test_list_backwards = [], [], []"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3099b8c-338e-4dfe-b705-b2ce8d2f1209",
      "metadata": {
        "id": "a3099b8c-338e-4dfe-b705-b2ce8d2f1209"
      },
      "source": [
        "Alright, now it's time for you to write your own code! Write code that appropriately populates the two lists just created. However, do so using just the *X_train* and *y_train* datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "f2bc429e-e1de-4036-85fb-cba8eaf461f4",
      "metadata": {
        "id": "f2bc429e-e1de-4036-85fb-cba8eaf461f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aea222b1-f97d-4f78-8116-1d9e234051fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'CAtBat', 'CRuns', 'CWalks', 'PutOuts', 'Assists']\n"
          ]
        }
      ],
      "source": [
        "#TODO: Implement the backward subset selection algorithm\n",
        "\n",
        "import statsmodels.api as sm # Fixed import\n",
        "\n",
        "def backward_stepwise_selection(X, y):\n",
        "\n",
        "  features = list(X.columns)\n",
        "  best_features = features.copy()\n",
        "  best_adj_r2 = -float('inf')\n",
        "\n",
        "  while len(features) > 0:\n",
        "    max_adj_r2 = -float('inf')\n",
        "    feature_to_remove = None\n",
        "\n",
        "    for feature in features:\n",
        "      remaining_features = [f for f in features if f != feature]\n",
        "      X_subset = X[remaining_features]\n",
        "      model = sm.OLS(y, sm.add_constant(X_subset)).fit()\n",
        "      adj_r2 = model.rsquared_adj\n",
        "\n",
        "      if adj_r2 > max_adj_r2:\n",
        "        max_adj_r2 = adj_r2\n",
        "        feature_to_remove = feature\n",
        "\n",
        "    if max_adj_r2 > best_adj_r2:\n",
        "      best_adj_r2 = max_adj_r2\n",
        "      best_features = [f for f in features if f != feature_to_remove]\n",
        "    else:\n",
        "      break\n",
        "\n",
        "    features.remove(feature_to_remove)\n",
        "\n",
        "  return best_features\n",
        "\n",
        "# Apply backward stepwise selection\n",
        "selected_features = backward_stepwise_selection(X_train, y_train)\n",
        "\n",
        "# Print the selected features\n",
        "print(\"Selected Features:\", selected_features)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "18d61483-50cd-4180-aa7d-94701fc575dc",
      "metadata": {
        "id": "18d61483-50cd-4180-aa7d-94701fc575dc"
      },
      "source": [
        "Next, create a dataframe with four columns: the feature list, $R^{2}$ values, and test $R^{2}$ values lists you just created, plus another column that records the number of features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "45a7c492-34d1-4a22-a0cf-cc7d27dc2b88",
      "metadata": {
        "id": "45a7c492-34d1-4a22-a0cf-cc7d27dc2b88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "86c9c4d3-23fe-470e-ff79-9814ccc2e099"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         R^2  Test R^2                                           Features  \\\n",
              "0   0.527931  0.592764  [AtBat, Hits, HmRun, Runs, Walks, Years, CAtBa...   \n",
              "1   0.527909  0.567587  [AtBat, Hits, HmRun, Runs, Walks, Years, CAtBa...   \n",
              "2   0.527807  0.552313  [AtBat, Hits, HmRun, Runs, Walks, CAtBat, CHmR...   \n",
              "3   0.527614  0.495073  [AtBat, Hits, HmRun, Runs, Walks, CAtBat, CRun...   \n",
              "4   0.527170  0.487824  [AtBat, Hits, Runs, Walks, CAtBat, CRuns, CRBI...   \n",
              "5   0.526541  0.482531  [AtBat, Hits, Runs, Walks, CAtBat, CRuns, CRBI...   \n",
              "6   0.525520  0.481514  [AtBat, Hits, Walks, CAtBat, CRuns, CRBI, CWal...   \n",
              "7   0.518807  0.428206  [AtBat, Hits, Walks, CAtBat, CRuns, CRBI, CWal...   \n",
              "8   0.509698  0.422981  [AtBat, Hits, Walks, CRuns, CRBI, CWalks, PutO...   \n",
              "9   0.497297  0.321898       [AtBat, Hits, Walks, CRuns, CWalks, PutOuts]   \n",
              "10  0.484059  0.301288               [AtBat, Hits, Walks, CRuns, PutOuts]   \n",
              "11  0.466405  0.294584                      [AtBat, Hits, CRuns, PutOuts]   \n",
              "12  0.448466  0.233529                             [Hits, CRuns, PutOuts]   \n",
              "13  0.414779  0.216319                                      [Hits, CRuns]   \n",
              "14  0.316606  0.178990                                            [CRuns]   \n",
              "\n",
              "    Num_Features  \n",
              "0             15  \n",
              "1             14  \n",
              "2             13  \n",
              "3             12  \n",
              "4             11  \n",
              "5             10  \n",
              "6              9  \n",
              "7              8  \n",
              "8              7  \n",
              "9              6  \n",
              "10             5  \n",
              "11             4  \n",
              "12             3  \n",
              "13             2  \n",
              "14             1  "
            ],
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              "\n",
              "  <div id=\"df-2fd4609b-2670-4648-9361-93faeaf36369\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <th></th>\n",
              "      <th>R^2</th>\n",
              "      <th>Test R^2</th>\n",
              "      <th>Features</th>\n",
              "      <th>Num_Features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.527931</td>\n",
              "      <td>0.592764</td>\n",
              "      <td>[AtBat, Hits, HmRun, Runs, Walks, Years, CAtBa...</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.527909</td>\n",
              "      <td>0.567587</td>\n",
              "      <td>[AtBat, Hits, HmRun, Runs, Walks, Years, CAtBa...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.527807</td>\n",
              "      <td>0.552313</td>\n",
              "      <td>[AtBat, Hits, HmRun, Runs, Walks, CAtBat, CHmR...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.527614</td>\n",
              "      <td>0.495073</td>\n",
              "      <td>[AtBat, Hits, HmRun, Runs, Walks, CAtBat, CRun...</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.527170</td>\n",
              "      <td>0.487824</td>\n",
              "      <td>[AtBat, Hits, Runs, Walks, CAtBat, CRuns, CRBI...</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.526541</td>\n",
              "      <td>0.482531</td>\n",
              "      <td>[AtBat, Hits, Runs, Walks, CAtBat, CRuns, CRBI...</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.525520</td>\n",
              "      <td>0.481514</td>\n",
              "      <td>[AtBat, Hits, Walks, CAtBat, CRuns, CRBI, CWal...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.518807</td>\n",
              "      <td>0.428206</td>\n",
              "      <td>[AtBat, Hits, Walks, CAtBat, CRuns, CRBI, CWal...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.509698</td>\n",
              "      <td>0.422981</td>\n",
              "      <td>[AtBat, Hits, Walks, CRuns, CRBI, CWalks, PutO...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.497297</td>\n",
              "      <td>0.321898</td>\n",
              "      <td>[AtBat, Hits, Walks, CRuns, CWalks, PutOuts]</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.484059</td>\n",
              "      <td>0.301288</td>\n",
              "      <td>[AtBat, Hits, Walks, CRuns, PutOuts]</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.466405</td>\n",
              "      <td>0.294584</td>\n",
              "      <td>[AtBat, Hits, CRuns, PutOuts]</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.448466</td>\n",
              "      <td>0.233529</td>\n",
              "      <td>[Hits, CRuns, PutOuts]</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.414779</td>\n",
              "      <td>0.216319</td>\n",
              "      <td>[Hits, CRuns]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.316606</td>\n",
              "      <td>0.178990</td>\n",
              "      <td>[CRuns]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_backwards",
              "summary": "{\n  \"name\": \"df_backwards\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"R^2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05916659982547259,\n        \"min\": 0.3166062064616073,\n        \"max\": 0.5279306798481691,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.49729651237237504,\n          0.4664050937014217,\n          0.5279306798481691\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test R^2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13549048813607792,\n        \"min\": 0.17899023961142346,\n        \"max\": 0.5927643979850825,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.32189793662385535,\n          0.2945839576266519,\n          0.5927643979850825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Features\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Features\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 15,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          6,\n          4,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#TODO: Create a dataframe consisting of the feature list, the R^2 values, and the number of features for each submodel $M_{i}$.\n",
        "\n",
        "# Create a list for the R^2 values\n",
        "R2_list_backwards = R2_list_backwards\n",
        "\n",
        "\n",
        "\n",
        "# Create a list for the number of features, starting from the length of features_list_backwards down to 1\n",
        "num_features = list(range(len(features_list_backwards), 0, -1))\n",
        "\n",
        "# Adjust num_features to match the length of R2 lists\n",
        "num_features = num_features[1:] # remove first element\n",
        "\n",
        "# Create the dataframe\n",
        "df_backwards = pd.DataFrame({\n",
        "    'R^2': R2_list_backwards,\n",
        "    'Test R^2': R2_test_list_backwards,\n",
        "    'Features': features_list_backwards[1:], #Start from the second element\n",
        "    'Num_Features': num_features,\n",
        "})\n",
        "\n",
        "# Display the dataframe\n",
        "df_backwards"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a18e39ef-5066-4e5b-9d56-ccba16e1e021",
      "metadata": {
        "id": "a18e39ef-5066-4e5b-9d56-ccba16e1e021"
      },
      "source": [
        "Finally, plot the adjusted $R^{2}$ values for each set of predictor variables, with the number of predictor variables on the $x$-axis. Mark the maximum, and print the predictors associated with that maximum.\n",
        "\n",
        "Recall that the adjusted $R^{2}$ is defined as:\n",
        "&nbsp;\n",
        "<center>\n",
        "    $\\displaystyle R^{2}_{a} = 1-\\frac{RSS/(n-k-1)}{TSS/n-1}$\n",
        "</center>\n",
        "&nbsp;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "c3a64684-f410-4797-8710-49f7fb703afb",
      "metadata": {
        "id": "c3a64684-f410-4797-8710-49f7fb703afb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "outputId": "082f46b3-57e4-478a-c34b-7b7f0813fd7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Adjusted R^2: 0.5279\n",
            "Predictors for Maximum Adjusted R^2: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# TODO: Plot the adjusted R^2 values for the number of predictor variables, mark the maximum, and print the predictors associated with that maximum.\n",
        "\n",
        "# Extract the relevant columns\n",
        "num_features = df_backwards['Num_Features']\n",
        "adj_r2_values = df_backwards['R^2']  # Assuming 'R^2' column contains adjusted R^2 values\n",
        "\n",
        "# Find the maximum adjusted R^2 and its index\n",
        "max_adj_r2 = max(adj_r2_values)\n",
        "max_index = adj_r2_values.idxmax()\n",
        "\n",
        "# Plot the adjusted R^2 values\n",
        "plt.plot(num_features, adj_r2_values, marker='o')\n",
        "plt.xlabel('Number of Predictor Variables')\n",
        "plt.ylabel('Adjusted R^2')\n",
        "plt.title('Adjusted R^2 vs. Number of Predictors (Backward Selection)')\n",
        "\n",
        "# Mark the maximum adjusted R^2\n",
        "plt.scatter(num_features[max_index], max_adj_r2, color='red', marker='x', s=100, label='Maximum Adjusted R^2')\n",
        "\n",
        "# Print the predictors associated with the maximum\n",
        "max_features = df_backwards.loc[max_index, 'Features']\n",
        "print(f\"Maximum Adjusted R^2: {max_adj_r2:.4f}\")\n",
        "print(f\"Predictors for Maximum Adjusted R^2: {max_features}\")\n",
        "\n",
        "# Add legend and show the plot\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d7b01f11-71ac-469c-9f1b-fa3cf6ca91e6",
      "metadata": {
        "id": "d7b01f11-71ac-469c-9f1b-fa3cf6ca91e6"
      },
      "source": [
        "Alright! Now, let's check if the number of predictors we determined using the adjusted $R^{2}$ value actually does the best on our test data.\n",
        "\n",
        "To do this, plot the actual $R^{2}$ value for each set of predictor variables when run on the test data. Again, make the x-axis the number of predictors, mark the maximum, and print the predictors associated with that maximum."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "ca68c870-b529-4e1b-b877-be753f654a38",
      "metadata": {
        "id": "ca68c870-b529-4e1b-b877-be753f654a38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "outputId": "d8439444-8365-4f63-d3dd-5b82fbc32385"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Test R^2: 0.5928\n",
            "Predictors for Maximum Test R^2: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# TODO: Plot the R^2 values for the given number of predictor variables when the model is run on the test data. Mark the maximum, and print the predictors associated with that maximum.\n",
        "# Extract the relevant columns\n",
        "num_features = df_backwards['Num_Features']\n",
        "test_r2_values = df_backwards['Test R^2']\n",
        "\n",
        "# Find the maximum test R^2 and its index\n",
        "max_test_r2 = max(test_r2_values)\n",
        "max_index = test_r2_values.idxmax()\n",
        "\n",
        "# Plot the test R^2 values\n",
        "plt.plot(num_features, test_r2_values, marker='o')\n",
        "plt.xlabel('Number of Predictor Variables')\n",
        "plt.ylabel('Test R^2')\n",
        "plt.title('Test R^2 vs. Number of Predictors (Backward Selection)')\n",
        "\n",
        "# Mark the maximum test R^2\n",
        "plt.scatter(num_features[max_index], max_test_r2, color='red', marker='x', s=100, label='Maximum Test R^2')\n",
        "\n",
        "# Print the predictors associated with the maximum\n",
        "max_features = df_backwards.loc[max_index, 'Features']\n",
        "print(f\"Maximum Test R^2: {max_test_r2:.4f}\")\n",
        "print(f\"Predictors for Maximum Test R^2: {max_features}\")\n",
        "\n",
        "# Add legend and show the plot\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d70c5ea-4963-411b-8d4c-5bdcc683244c",
      "metadata": {
        "id": "1d70c5ea-4963-411b-8d4c-5bdcc683244c"
      },
      "source": [
        "Now, plot both on the same line charts on the same figure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "ece7197d-61b6-43d3-802f-640bce0ab701",
      "metadata": {
        "id": "ece7197d-61b6-43d3-802f-640bce0ab701",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9b1c0ed0-d15d-4430-9c66-641b9d4f1eee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Adjusted R^2: 0.5279, Predictors: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n",
            "Maximum Test R^2: 0.5928, Predictors: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n",
            "Maximum Adjusted R^2: 0.5279, Predictors: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n",
            "Maximum Test R^2: 0.5928, Predictors: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n",
            "Maximum Adjusted R^2: 0.5279, Predictors: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n",
            "Maximum Test R^2: 0.5928, Predictors: ['AtBat', 'Hits', 'HmRun', 'Runs', 'Walks', 'Years', 'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'PutOuts', 'Assists', 'Errors']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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QDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQjCGFS1NTU1RVVUVJSUlUV1fH9u3bjzp3w4YNcdFFF8XkyZNj8uTJUVtb+6bzAQCOJu9w2bRpU9TX10dDQ0Ps3LkzZs6cGXV1dfHSSy8NOn/btm1x+eWXx+9///toaWmJysrK+NSnPhV/+9vf3vbmAYCJpSDLsiyfBdXV1XH++efHunXrIiKir68vKisr4/rrr4/ly5e/5fre3t6YPHlyrFu3LhYtWjTonO7u7uju7u7/c2dnZ1RWVkZHR0eUlpbms10AYIx0dnZGWVnZsP78zusZl56entixY0fU1tb+9w4KC6O2tjZaWlqO6T5effXVeP311+Md73jHUec0NjZGWVlZ/62ysjKfbQIA41Re4XLgwIHo7e2N8vLyAePl5eXR1tZ2TPdx4403xvTp0wfEz/9asWJFdHR09N/279+fzzYBgHFq0mhebM2aNbFx48bYtm1blJSUHHVeLpeLXC43ijsDAFKQV7hMmTIlioqKor29fcB4e3t7VFRUvOnaO+64I9asWRO//e1v49xzz81/pwDAhJfXS0XFxcUxe/bsaG5u7h/r6+uL5ubmqKmpOeq622+/PW677bbYunVrzJkzZ+i7BQAmtLxfKqqvr4/FixfHnDlzYu7cubF27dro6uqKJUuWRETEokWLYsaMGdHY2BgREd/97ndj1apV8bOf/Syqqqr63wtz8sknx8knnzyMDwUAGO/yDpcFCxbEyy+/HKtWrYq2traYNWtWbN26tf8Nu/v27YvCwv8+kfPDH/4wenp64rOf/eyA+2loaIhvfvObb2/3AMCEkvf3uIyFkfgcOAAwssb8e1wAAMaScAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkDClcmpqaoqqqKkpKSqK6ujq2b9/+pvMfeOCBOOuss6KkpCTOOeec2LJly5A2CwBMbHmHy6ZNm6K+vj4aGhpi586dMXPmzKirq4uXXnpp0PmPP/54XH755XHllVfGrl27Yv78+TF//vx46qmn3vbmAYCJpSDLsiyfBdXV1XH++efHunXrIiKir68vKisr4/rrr4/ly5cfMX/BggXR1dUVv/71r/vHPvrRj8asWbNi/fr1g16ju7s7uru7+//c0dERp512Wuzfvz9KS0vz2S4AMEY6OzujsrIyDh48GGVlZcNyn5PymdzT0xM7duyIFStW9I8VFhZGbW1ttLS0DLqmpaUl6uvrB4zV1dXFL3/5y6Nep7GxMW699dYjxisrK/PZLgBwHPjHP/4xNuFy4MCB6O3tjfLy8gHj5eXlsWfPnkHXtLW1DTq/ra3tqNdZsWLFgNg5ePBgvOc974l9+/YN2wNnaN6oZ89+jT1ncfxwFscX53H8eOMVk3e84x3Ddp95hctoyeVykcvljhgvKyvzD+FxorS01FkcJ5zF8cNZHF+cx/GjsHD4PsSc1z1NmTIlioqKor29fcB4e3t7VFRUDLqmoqIir/kAAEeTV7gUFxfH7Nmzo7m5uX+sr68vmpubo6amZtA1NTU1A+ZHRDz66KNHnQ8AcDR5v1RUX18fixcvjjlz5sTcuXNj7dq10dXVFUuWLImIiEWLFsWMGTOisbExIiJuuOGGuPjii+POO++Myy67LDZu3BhPPPFE3HPPPcd8zVwuFw0NDYO+fMTochbHD2dx/HAWxxfncfwYibPI++PQERHr1q2L733ve9HW1hazZs2K73//+1FdXR0RER//+Mejqqoq7r///v75DzzwQNx8883x4osvxvvf//64/fbb49JLLx22BwEATAxDChcAgLHgdxUBAMkQLgBAMoQLAJAM4QIAJOO4CZempqaoqqqKkpKSqK6uju3bt7/p/AceeCDOOuusKCkpiXPOOSe2bNkySjsd//I5iw0bNsRFF10UkydPjsmTJ0dtbe1bnh3HLt+/F2/YuHFjFBQUxPz580d2gxNIvmdx8ODBWLp0aUybNi1yuVyceeaZ/j01TPI9i7Vr18YHPvCBOPHEE6OysjKWLVsWr7322ijtdvz6wx/+EPPmzYvp06dHQUHBm/4Owjds27YtPvKRj0Qul4v3ve99Az6BfMyy48DGjRuz4uLi7L777sv+/Oc/Z1dffXV26qmnZu3t7YPO/+Mf/5gVFRVlt99+e/b0009nN998c3bCCSdkTz755CjvfPzJ9yyuuOKKrKmpKdu1a1e2e/fu7Itf/GJWVlaW/fWvfx3lnY8/+Z7FG1544YVsxowZ2UUXXZR95jOfGZ3NjnP5nkV3d3c2Z86c7NJLL80ee+yx7IUXXsi2bduWtba2jvLOx598z+KnP/1plsvlsp/+9KfZCy+8kD3yyCPZtGnTsmXLlo3yzsefLVu2ZCtXrsweeuihLCKyhx9++E3n7927NzvppJOy+vr67Omnn85+8IMfZEVFRdnWrVvzuu5xES5z587Nli5d2v/n3t7ebPr06VljY+Og8z/3uc9ll1122YCx6urq7Etf+tKI7nMiyPcs/tfhw4ezU045JfvJT34yUlucMIZyFocPH84uuOCC7Ec/+lG2ePFi4TJM8j2LH/7wh9npp5+e9fT0jNYWJ4x8z2Lp0qXZJz7xiQFj9fX12YUXXjii+5xojiVcvvGNb2Qf/vCHB4wtWLAgq6ury+taY/5SUU9PT+zYsSNqa2v7xwoLC6O2tjZaWloGXdPS0jJgfkREXV3dUedzbIZyFv/r1Vdfjddff31YfxPoRDTUs/jWt74VU6dOjSuvvHI0tjkhDOUsfvWrX0VNTU0sXbo0ysvL4+yzz47Vq1dHb2/vaG17XBrKWVxwwQWxY8eO/peT9u7dG1u2bPElqGNguH52j/lvhz5w4ED09vZGeXn5gPHy8vLYs2fPoGva2toGnd/W1jZi+5wIhnIW/+vGG2+M6dOnH/EPJ/kZylk89thjce+990Zra+so7HDiGMpZ7N27N373u9/FF77whdiyZUs899xz8eUvfzlef/31aGhoGI1tj0tDOYsrrrgiDhw4EB/72Mciy7I4fPhwXHvttXHTTTeNxpb5f472s7uzszP+/e9/x4knnnhM9zPmz7gwfqxZsyY2btwYDz/8cJSUlIz1diaUQ4cOxcKFC2PDhg0xZcqUsd7OhNfX1xdTp06Ne+65J2bPnh0LFiyIlStXxvr168d6axPOtm3bYvXq1XH33XfHzp0746GHHorNmzfHbbfdNtZbY4jG/BmXKVOmRFFRUbS3tw8Yb29vj4qKikHXVFRU5DWfYzOUs3jDHXfcEWvWrInf/va3ce65547kNieEfM/i+eefjxdffDHmzZvXP9bX1xcREZMmTYpnnnkmzjjjjJHd9Dg1lL8X06ZNixNOOCGKior6xz74wQ9GW1tb9PT0RHFx8YjuebwaylnccsstsXDhwrjqqqsiIuKcc86Jrq6uuOaaa2LlypVRWOi/30fL0X52l5aWHvOzLRHHwTMuxcXFMXv27Ghubu4f6+vri+bm5qipqRl0TU1NzYD5ERGPPvroUedzbIZyFhERt99+e9x2222xdevWmDNnzmhsddzL9yzOOuusePLJJ6O1tbX/9ulPfzouueSSaG1tjcrKytHc/rgylL8XF154YTz33HP98RgR8eyzz8a0adNEy9swlLN49dVXj4iTN4Iy86v6RtWw/ezO733DI2Pjxo1ZLpfL7r///uzpp5/OrrnmmuzUU0/N2trasizLsoULF2bLly/vn//HP/4xmzRpUnbHHXdku3fvzhoaGnwcepjkexZr1qzJiouLswcffDD7+9//3n87dOjQWD2EcSPfs/hfPlU0fPI9i3379mWnnHJK9pWvfCV75plnsl//+tfZ1KlTs29/+9tj9RDGjXzPoqGhITvllFOyn//859nevXuz3/zmN9kZZ5yRfe5znxurhzBuHDp0KNu1a1e2a9euLCKyu+66K9u1a1f2l7/8JcuyLFu+fHm2cOHC/vlvfBz661//erZ79+6sqakp3Y9DZ1mW/eAHP8hOO+20rLi4OJs7d272pz/9qf9/u/jii7PFixcPmP+LX/wiO/PMM7Pi4uLswx/+cLZ58+ZR3vH4lc9ZvOc978ki4ohbQ0PD6G98HMr378X/J1yGV75n8fjjj2fV1dVZLpfLTj/99Ow73/lOdvjw4VHe9fiUz1m8/vrr2Te/+c3sjDPOyEpKSrLKysrsy1/+cvbPf/5z9Dc+zvz+978f9N//b/z/v3jx4uziiy8+Ys2sWbOy4uLi7PTTT89+/OMf533dgizzXBkAkIYxf48LAMCxEi4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJCM/wM9kKRvAVrZIAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# TODO: Plot both line charts on the same figure.\n",
        "\n",
        "# Create the figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Extract the relevant columns\n",
        "num_features = df_backwards['Num_Features']\n",
        "adj_r2_values = df_backwards['R^2']\n",
        "test_r2_values = df_backwards['Test R^2']\n",
        "\n",
        "# Find the maximum values and their indices\n",
        "max_adj_r2 = max(adj_r2_values)\n",
        "max_adj_index = adj_r2_values.idxmax()\n",
        "max_test_r2 = max(test_r2_values)\n",
        "max_test_index = test_r2_values.idxmax()\n",
        "\n",
        "# Create the plot\n",
        "fig, ax = plt.subplots()  # Create a figure and an axes object\n",
        "\n",
        "# Plot adjusted R^2\n",
        "ax.plot(num_features, adj_r2_values, marker='o', label='Adjusted R^2')\n",
        "ax.scatter(num_features[max_adj_index], max_adj_r2, color='red', marker='x', s=100)\n",
        "\n",
        "# Plot test R^2\n",
        "ax.plot(num_features, test_r2_values, marker='o', label='Test R^2')\n",
        "ax.scatter(num_features[max_test_index], max_test_r2, color='blue', marker='x', s=100)\n",
        "\n",
        "# Set labels and title\n",
        "ax.set_xlabel('Number of Predictor Variables')\n",
        "ax.set_ylabel('R^2 Values')\n",
        "ax.set_title('Adjusted R^2 and Test R^2 vs. Number of Predictors')\n",
        "\n",
        "# TODO: Plot both line charts on the same figure.\n",
        "\n",
        "# Create the figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Extract the relevant columns\n",
        "num_features = df_backwards['Num_Features']\n",
        "adj_r2_values = df_backwards['R^2']\n",
        "test_r2_values = df_backwards['Test R^2']\n",
        "\n",
        "# Find the maximum values and their indices\n",
        "max_adj_r2 = max(adj_r2_values)\n",
        "max_adj_index = adj_r2_values.idxmax()\n",
        "max_test_r2 = max(test_r2_values)\n",
        "max_test_index = test_r2_values.idxmax()\n",
        "\n",
        "# Create the plot\n",
        "#fig, ax = plt.subplots()  # Create a figure and an axes object - already created\n",
        "\n",
        "# Plot adjusted R^2\n",
        "ax.plot(num_features, adj_r2_values, marker='o', label='Adjusted R^2')\n",
        "ax.scatter(num_features[max_adj_index], max_adj_r2, color='red', marker='x', s=100)\n",
        "\n",
        "# Plot test R^2\n",
        "ax.plot(num_features, test_r2_values, marker='o', label='Test R^2')\n",
        "ax.scatter(num_features[max_test_index], max_test_r2, color='blue', marker='x', s=100)\n",
        "\n",
        "# Set labels and title\n",
        "ax.set_xlabel('Number of Predictor Variables')\n",
        "ax.set_ylabel('R^2 Values')\n",
        "ax.set_title('Adjusted R^2 and Test R^2 vs. Number of Predictors')\n",
        "\n",
        "# Add legend and grid\n",
        "ax.legend()\n",
        "ax.grid(True)\n",
        "\n",
        "# Print the information for maximum values\n",
        "print(f\"Maximum Adjusted R^2: {max_adj_r2:.4f}, Predictors: {df_backwards.loc[max_adj_index, 'Features']}\")\n",
        "print(f\"Maximum Test R^2: {max_test_r2:.4f}, Predictors: {df_backwards.loc[max_test_index, 'Features']}\")\n",
        "\n",
        "# Create the figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Extract the relevant columns\n",
        "num_features = df_backwards['Num_Features']\n",
        "adj_r2_values = df_backwards['R^2']\n",
        "test_r2_values = df_backwards['Test R^2']\n",
        "\n",
        "# Find the maximum values and their indices\n",
        "max_adj_r2 = max(adj_r2_values)\n",
        "max_adj_index = adj_r2_values.idxmax()\n",
        "max_test_r2 = max(test_r2_values)\n",
        "max_test_index = test_r2_values.idxmax()\n",
        "\n",
        "# Create the plot\n",
        "#fig, ax = plt.subplots()  # Create a figure and an axes object - already created\n",
        "\n",
        "# Plot adjusted R^2\n",
        "ax.plot(num_features, adj_r2_values, marker='o', label='Adjusted R^2')\n",
        "ax.scatter(num_features[max_adj_index], max_adj_r2, color='red', marker='x', s=100)\n",
        "\n",
        "# Plot test R^2\n",
        "ax.plot(num_features, test_r2_values, marker='o', label='Test R^2')\n",
        "ax.scatter(num_features[max_test_index], max_test_r2, color='blue', marker='x', s=100)\n",
        "\n",
        "# Set labels and title\n",
        "ax.set_xlabel('Number of Predictor Variables')\n",
        "ax.set_ylabel('R^2 Values')\n",
        "ax.set_title('Adjusted R^2 and Test R^2 vs. Number of Predictors')\n",
        "\n",
        "# Add legend and grid\n",
        "ax.legend()\n",
        "ax.grid(True)\n",
        "\n",
        "# Print the information for maximum values\n",
        "print(f\"Maximum Adjusted R^2: {max_adj_r2:.4f}, Predictors: {df_backwards.loc[max_adj_index, 'Features']}\")\n",
        "print(f\"Maximum Test R^2: {max_test_r2:.4f}, Predictors: {df_backwards.loc[max_test_index, 'Features']}\")\n",
        "\n",
        "# Create the figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Extract the relevant columns\n",
        "num_features = df_backwards['Num_Features']\n",
        "adj_r2_values = df_backwards['R^2']\n",
        "test_r2_values = df_backwards['Test R^2']\n",
        "\n",
        "# Find the maximum values and their indices\n",
        "max_adj_r2 = max(adj_r2_values)\n",
        "max_adj_index = adj_r2_values.idxmax()\n",
        "max_test_r2 = max(test_r2_values)\n",
        "max_test_index = test_r2_values.idxmax()\n",
        "\n",
        "# Create the plot\n",
        "fig, ax = plt.subplots()  # Create a figure and an axes object\n",
        "\n",
        "# Plot adjusted R^2\n",
        "ax.plot(num_features, adj_r2_values, marker='o', label='Adjusted R^2')\n",
        "ax.scatter(num_features[max_adj_index], max_adj_r2, color='red', marker='x', s=100)\n",
        "\n",
        "# Plot test R^2\n",
        "ax.plot(num_features, test_r2_values, marker='o', label='Test R^2')\n",
        "ax.scatter(num_features[max_test_index], max_test_r2, color='blue', marker='x', s=100)\n",
        "\n",
        "# Set labels and title\n",
        "ax.set_xlabel('Number of Predictor Variables')\n",
        "ax.set_ylabel('R^2 Values')\n",
        "ax.set_title('Adjusted R^2 and Test R^2 vs. Number of Predictors')\n",
        "\n",
        "# Add legend and grid\n",
        "ax.legend()\n",
        "ax.grid(True)\n",
        "\n",
        "# Print the information for maximum values\n",
        "print(f\"Maximum Adjusted R^2: {max_adj_r2:.4f}, Predictors: {df_backwards.loc[max_adj_index, 'Features']}\")\n",
        "print(f\"Maximum Test R^2: {max_test_r2:.4f}, Predictors: {df_backwards.loc[max_test_index, 'Features']}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6cebf9ae-8956-43a9-a4cc-79f773158f9e",
      "metadata": {
        "id": "6cebf9ae-8956-43a9-a4cc-79f773158f9e"
      },
      "source": [
        "Do the best subset of predictors found using adjusted $R^{2}$ match those found using a test set?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "45ae7f6c-9e2e-4573-ab89-db8e578dfb16",
      "metadata": {
        "id": "45ae7f6c-9e2e-4573-ab89-db8e578dfb16"
      },
      "source": [
        "## Ridge and Lasso Regression ##\n",
        "\n",
        "Next, we'll take a look at ridge and lasso regression for this hitters dataset. We'll use ridge regression as an example, and then you should do something similar for lasso."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3bb9162e-0768-420e-839d-f2b4e2e37f50",
      "metadata": {
        "id": "3bb9162e-0768-420e-839d-f2b4e2e37f50"
      },
      "source": [
        "**Ridge Regression**\n",
        "\n",
        "We'll first investigate building a predictive salary model using ridge regression.\n",
        "\n",
        "To do this, first we'll create a set of 90 values for our hyperparameter $\\lambda$ as we did in Lecture 16."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "1588d501",
      "metadata": {
        "id": "1588d501"
      },
      "outputs": [],
      "source": [
        "lambdas = 10**np.linspace(7,-2,90)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "345ceb3b-4074-4e93-a741-7ecca08ae331",
      "metadata": {
        "id": "345ceb3b-4074-4e93-a741-7ecca08ae331"
      },
      "source": [
        "Now, we'll calculate the values of our coefficients for each value of $\\lambda$:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "be8351bd",
      "metadata": {
        "id": "be8351bd"
      },
      "outputs": [],
      "source": [
        "ridge = Ridge()\n",
        "coefs = [] #Keep track of the coefficients.\n",
        "\n",
        "for l in lambdas:\n",
        "    ridge.set_params(alpha = l) #Ridge regression in Python calls the lambda term alpha.\n",
        "    ridge.fit(X, y)\n",
        "    coefs.append(ridge.coef_)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ecb510f9-4163-44e4-a28e-dd375b8df1c7",
      "metadata": {
        "id": "ecb510f9-4163-44e4-a28e-dd375b8df1c7"
      },
      "source": [
        "We can plot how these coefficients change as $\\lambda$ increases, noting they should approach but not equal $0$ as $\\lambda$ gets large."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "111dd38e",
      "metadata": {
        "id": "111dd38e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "74aa4b15-4551-4525-f090-515acd0453ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = plt.gca()\n",
        "ax.plot(lambdas, coefs)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.xlabel('alpha')\n",
        "plt.ylabel('weights');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9bc472c0-e12a-4eb4-86db-a16d2bdec21b",
      "metadata": {
        "id": "9bc472c0-e12a-4eb4-86db-a16d2bdec21b"
      },
      "source": [
        "We can use the RidgeCV function to determine an optimal value for $\\lambda$ based on leave-one-out cross-validation (LOOCV):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "d3c1b59c",
      "metadata": {
        "id": "d3c1b59c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b48a1176-5f65-4895-d131-1c58d593bd0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11679.270855560682"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "ridgecv = RidgeCV(alphas = lambdas)\n",
        "ridgecv.fit(X, y)\n",
        "ridgecv.alpha_"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3aadb658-83fd-4ef0-a3f7-00e358c52ca9",
      "metadata": {
        "id": "3aadb658-83fd-4ef0-a3f7-00e358c52ca9"
      },
      "source": [
        "Building a ridge regression model with this optimal $\\lambda$, we get:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "0ab75cac",
      "metadata": {
        "id": "0ab75cac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eca22ec2-68eb-4dfa-c192-29fcea9285c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AtBat     -1.443360\n",
            "Hits       4.571630\n",
            "HmRun     -0.140006\n",
            "Runs       0.523664\n",
            "RBI        0.553955\n",
            "Walks      4.064171\n",
            "Years     -0.028306\n",
            "CAtBat    -0.271155\n",
            "CHits      0.555396\n",
            "CHmRun     0.181851\n",
            "CRuns      1.164392\n",
            "CRBI       0.645214\n",
            "CWalks    -0.513905\n",
            "PutOuts    0.293705\n",
            "Assists    0.322846\n",
            "Errors    -1.206413\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96812.61013827637"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "ridge2 = Ridge(alpha = ridgecv.alpha_)\n",
        "ridge2.fit(X, y)\n",
        "print(pd.Series(ridge2.coef_, index = X.columns))\n",
        "mean_squared_error(y, ridge2.predict(X))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c118072-4bb0-4629-a7fc-2237f3f55329",
      "metadata": {
        "id": "1c118072-4bb0-4629-a7fc-2237f3f55329"
      },
      "source": [
        "For $3$-fold cross-validation we get:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "db7948e1",
      "metadata": {
        "id": "db7948e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0847107c-27e0-47c4-e052-5692c003a3c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9253.203955702538"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "ridgecv = RidgeCV(alphas = lambdas, cv=3)\n",
        "ridgecv.fit(X, y)\n",
        "ridgecv.alpha_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "9ac60ba3",
      "metadata": {
        "id": "9ac60ba3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d49fad5-5fda-41a9-d7dc-3e726d67f422"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AtBat     -1.551898\n",
            "Hits       4.940360\n",
            "HmRun     -0.117700\n",
            "Runs       0.344884\n",
            "RBI        0.558745\n",
            "Walks      4.324953\n",
            "Years     -0.051075\n",
            "CAtBat    -0.260520\n",
            "CHits      0.508350\n",
            "CHmRun     0.173880\n",
            "CRuns      1.203832\n",
            "CRBI       0.649061\n",
            "CWalks    -0.548922\n",
            "PutOuts    0.294127\n",
            "Assists    0.330310\n",
            "Errors    -1.359501\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96561.53447785083"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "ridge3 = Ridge(alpha = ridgecv.alpha_)\n",
        "ridge3.fit(X, y)\n",
        "print(pd.Series(ridge3.coef_, index = X.columns))\n",
        "mean_squared_error(y, ridge3.predict(X))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c61b4011-6029-4eff-b861-046f0d5bdebc",
      "metadata": {
        "id": "c61b4011-6029-4eff-b861-046f0d5bdebc"
      },
      "source": [
        "For the next part of this homework assignment, you'll investigate the same modeling question using lasso regression and the LassoCV function. Check out the optimal $\\lambda$ values you get for lasso regression with LOOCV and 3-fold CV, and note which coefficients are set to $0$ is each case.\n",
        "\n",
        "Specifically, you should:\n",
        "\n",
        "1. Plot how the weights change for lasso regression as $\\lambda$ gets large.\n",
        "2. Calculate the optimal value of $\\lambda$, and the corresponding model coefficients, using LOOCV.\n",
        "3. Calculate the optimal value of $\\lambda$, and the corresponding model coefficients, using 3-fold CV."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "64670ca9-9dcb-4c45-bfa6-8f0bc73bb32c",
      "metadata": {
        "id": "64670ca9-9dcb-4c45-bfa6-8f0bc73bb32c"
      },
      "source": [
        "*Note*: Remember, for lasso, you'll probably need to set that max_iter parameter. A value like 100,000 should be good."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "bb9e5df5",
      "metadata": {
        "id": "bb9e5df5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d009b907-657d-4068-fc2c-d214e9cb6dcd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal lambda (LOOCV): 110.90313431155938\n",
            "Coefficients (LOOCV):\n",
            " AtBat     -1.682536\n",
            "Hits       5.750015\n",
            "HmRun      0.000000\n",
            "Runs      -0.000000\n",
            "RBI        0.000000\n",
            "Walks      4.671893\n",
            "Years     -0.000000\n",
            "CAtBat    -0.229756\n",
            "CHits      0.350818\n",
            "CHmRun     0.000000\n",
            "CRuns      1.272066\n",
            "CRBI       0.743077\n",
            "CWalks    -0.583380\n",
            "PutOuts    0.289941\n",
            "Assists    0.261729\n",
            "Errors    -0.000000\n",
            "dtype: float64\n",
            "\n",
            "Optimal lambda (3-fold CV): 139.9804598013952\n",
            "Coefficients (3-fold CV):\n",
            " AtBat     -1.550716\n",
            "Hits       5.411148\n",
            "HmRun      0.000000\n",
            "Runs       0.000000\n",
            "RBI        0.000000\n",
            "Walks      4.417388\n",
            "Years     -0.000000\n",
            "CAtBat    -0.238500\n",
            "CHits      0.388885\n",
            "CHmRun     0.000000\n",
            "CRuns      1.236410\n",
            "CRBI       0.736682\n",
            "CWalks    -0.543039\n",
            "PutOuts    0.288647\n",
            "Assists    0.251858\n",
            "Errors    -0.000000\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#TODO: Plot how the weights change for lasso regression as lambda gets large here.\n",
        "# 1. Plot how the weights change for lasso regression as lambda gets large.\n",
        "lasso = Lasso(max_iter=100000)\n",
        "coefs = []\n",
        "\n",
        "for l in lambdas:\n",
        "    lasso.set_params(alpha=l)\n",
        "    lasso.fit(X, y)\n",
        "    coefs.append(lasso.coef_)\n",
        "\n",
        "ax = plt.gca()\n",
        "ax.plot(lambdas, coefs)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.xlabel('alpha')\n",
        "plt.ylabel('weights');\n",
        "\n",
        "# 2. Calculate the optimal value of lambda, and the corresponding model coefficients, using LOOCV.\n",
        "lassocv = LassoCV(alphas=lambdas, cv=None, max_iter=100000)  # LOOCV (cv=None)\n",
        "lassocv.fit(X, y)\n",
        "print(\"Optimal lambda (LOOCV):\", lassocv.alpha_)\n",
        "print(\"Coefficients (LOOCV):\\n\", pd.Series(lassocv.coef_, index=X.columns))\n",
        "\n",
        "# 3. Calculate the optimal value of lambda, and the corresponding model coefficients, using 3-fold CV.\n",
        "lassocv = LassoCV(alphas=lambdas, cv=3, max_iter=100000)  # 3-fold CV\n",
        "lassocv.fit(X, y)\n",
        "print(\"\\nOptimal lambda (3-fold CV):\", lassocv.alpha_)\n",
        "print(\"Coefficients (3-fold CV):\\n\", pd.Series(lassocv.coef_, index=X.columns))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "dd7135db",
      "metadata": {
        "id": "dd7135db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fbc1184-0688-4860-d746-bd24fa2ab370"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal lambda (LOOCV): 110.90313431155938\n",
            "Coefficients (LOOCV):\n",
            " AtBat     -1.682536\n",
            "Hits       5.750015\n",
            "HmRun      0.000000\n",
            "Runs      -0.000000\n",
            "RBI        0.000000\n",
            "Walks      4.671893\n",
            "Years     -0.000000\n",
            "CAtBat    -0.229756\n",
            "CHits      0.350818\n",
            "CHmRun     0.000000\n",
            "CRuns      1.272066\n",
            "CRBI       0.743077\n",
            "CWalks    -0.583380\n",
            "PutOuts    0.289941\n",
            "Assists    0.261729\n",
            "Errors    -0.000000\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "#TODO: Calculate the optimal value of lambda, and the corresponding model coefficients, using LOOCV.\n",
        "# Assuming 'X' and 'y' are your predictor and response variables, and 'lambdas' is defined\n",
        "lassocv = LassoCV(alphas=lambdas, cv=None, max_iter=100000)  # LOOCV (cv=None)\n",
        "lassocv.fit(X, y)\n",
        "\n",
        "print(\"Optimal lambda (LOOCV):\", lassocv.alpha_)\n",
        "print(\"Coefficients (LOOCV):\\n\", pd.Series(lassocv.coef_, index=X.columns))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "5fc4d6cb",
      "metadata": {
        "id": "5fc4d6cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b63b453-b858-4e37-f0da-a8c846436b22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal lambda (3-fold CV): 139.9804598013952\n",
            "Coefficients (3-fold CV):\n",
            " AtBat     -1.550716\n",
            "Hits       5.411148\n",
            "HmRun      0.000000\n",
            "Runs       0.000000\n",
            "RBI        0.000000\n",
            "Walks      4.417388\n",
            "Years     -0.000000\n",
            "CAtBat    -0.238500\n",
            "CHits      0.388885\n",
            "CHmRun     0.000000\n",
            "CRuns      1.236410\n",
            "CRBI       0.736682\n",
            "CWalks    -0.543039\n",
            "PutOuts    0.288647\n",
            "Assists    0.251858\n",
            "Errors    -0.000000\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "#TODO: Calculate the optimal value of lambda, and the corresponding model coefficients, using 3-fold CV.\n",
        "# Assuming 'X' and 'y' are your predictor and response variables, and 'lambdas' is defined\n",
        "lassocv = LassoCV(alphas=lambdas, cv=3, max_iter=100000)  # 3-fold CV\n",
        "lassocv.fit(X, y)\n",
        "\n",
        "print(\"Optimal lambda (3-fold CV):\", lassocv.alpha_)\n",
        "print(\"Coefficients (3-fold CV):\\n\", pd.Series(lassocv.coef_, index=X.columns))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "569d9fbc-c967-439c-8fbe-8d04e41eb989",
      "metadata": {
        "id": "569d9fbc-c967-439c-8fbe-8d04e41eb989"
      },
      "source": [
        "How do the predictors with non-zero coefficients compare with the ones found earlier in the assignment using backward stepwise selection?"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "anaconda-panel-2023.05-py310",
      "language": "python",
      "name": "conda-env-anaconda-panel-2023.05-py310-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}