heart_failure_ml_models.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.model_selection import train_test_split, cross_validate, cross_val_score\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, StratifiedKFold\n",
        "from sklearn.pipeline import make_pipeline, Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, RocCurveDisplay, roc_curve\n",
        "from sklearn.metrics import precision_recall_fscore_support, classification_report, make_scorer\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
        "import xgboost as xgb\n",
        "from xgboost import XGBClassifier\n",
        "from imblearn.over_sampling import SMOTENC\n",
        "\n",
        "df = pd.read_csv('data/preprocessed_heart_failure_dataset.csv')\n",
        "feature_column_names = ['age', 'anemia', 'creatine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium']\n",
        "continuous_features = ['age', 'creatine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium']\n",
        "\n",
        "X = df.copy().drop(columns=['patient_deceased'])\n",
        "y = df['patient_deceased'].copy()\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
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              "    age  anemia  creatine_phosphokinase  ejection_fraction  platelets  \\\n",
              "0  75.0       0                     582                 20   265000.0   \n",
              "1  65.0       0                     146                 20   162000.0   \n",
              "2  50.0       1                     111                 20   210000.0   \n",
              "3  90.0       1                      47                 40   204000.0   \n",
              "4  75.0       1                     246                 15   127000.0   \n",
              "\n",
              "   serum_creatinine  serum_sodium  days_until_follow_up  \n",
              "0               1.9           130                     4  \n",
              "1               1.3           129                     7  \n",
              "2               1.9           137                     7  \n",
              "3               2.1           132                     8  \n",
              "4               1.2           137                    10  "
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              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 223,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.934849892702381,\n        \"min\": 40.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          73.0,\n          59.0,\n          51.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anemia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"creatine_phosphokinase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 279,\n        \"min\": 30,\n        \"max\": 1202,\n        \"num_unique_values\": 155,\n        \"samples\": [\n          943,\n          213\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ejection_fraction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 14,\n        \"max\": 65,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          60,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"platelets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67302.0213625369,\n        \"min\": 122000.0,\n        \"max\": 427000.0,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          126000.0,\n          300000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"serum_creatinine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32802397059639393,\n        \"min\": 0.6,\n        \"max\": 2.1,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          1.9,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"serum_sodium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 126,\n        \"max\": 148,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          130,\n          141\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days_until_follow_up\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76,\n        \"min\": 4,\n        \"max\": 285,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          85,\n          88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 223 entries, 0 to 222\n",
            "Data columns (total 8 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   age                     223 non-null    float64\n",
            " 1   anemia                  223 non-null    int64  \n",
            " 2   creatine_phosphokinase  223 non-null    int64  \n",
            " 3   ejection_fraction       223 non-null    int64  \n",
            " 4   platelets               223 non-null    float64\n",
            " 5   serum_creatinine        223 non-null    float64\n",
            " 6   serum_sodium            223 non-null    int64  \n",
            " 7   days_until_follow_up    223 non-null    int64  \n",
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      "source": [
        "standardizer = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('scale', StandardScaler(), continuous_features)\n",
        "    ],\n",
        "    remainder='passthrough'\n",
        ")\n",
        "\n",
        "def create_table(table_data: list[list], title: str):\n",
        "  fig, ax = plt.subplots()\n",
        "  ax.axis('off')\n",
        "  table = ax.table(cellText=table_data, loc='center', cellLoc='center', colLabels=None)\n",
        "  ax.set_title(title)\n",
        "  table.auto_set_font_size(False)\n",
        "  table.set_fontsize(10)\n",
        "  table.scale(1.2, 1.2)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "7dfB8lW369G_"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_metrics_data = []\n",
        "test_metrics_columns = ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC', 'Model']\n",
        "\n",
        "def create_test_metrics_row(y_test, y_pred, model_name):\n",
        "  return [\n",
        "    round(accuracy_score(y_test, y_pred), 3),\n",
        "    round(precision_score(y_test, y_pred), 3),\n",
        "    round(recall_score(y_test, y_pred), 3),\n",
        "    round(f1_score(y_test, y_pred), 3),\n",
        "    round(roc_auc_score(y_test, y_pred), 3),\n",
        "    model_name\n",
        "  ]"
      ],
      "metadata": {
        "id": "HVI9sASdFEP-"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ],
      "metadata": {
        "id": "RhxsdLJqiwZS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning With Stratified K-Fold CV and Grid Search CV"
      ],
      "metadata": {
        "id": "FOyQqA0OwTb2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_pipeline = Pipeline([\n",
        "    ('scale', standardizer),\n",
        "    ('logistic', LogisticRegression())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'logistic__C': [0.001, 0.01, 0.1, 0.5, 1, 10, 25],\n",
        "}\n",
        "\n",
        "stratkf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "\n",
        "scoring = {\n",
        "        'accuracy': make_scorer(accuracy_score),\n",
        "        'recall': make_scorer(recall_score),\n",
        "        'precision': make_scorer(precision_score, zero_division=1),\n",
        "        'f1': make_scorer(f1_score),\n",
        "        'roc_auc': make_scorer(roc_auc_score, needs_proba=True)\n",
        "}\n",
        "\n",
        "logistic_grid_search = GridSearchCV(logistic_pipeline, param_grid, scoring=scoring, refit='accuracy', cv=stratkf, n_jobs=-1)\n",
        "logistic_grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "uVRkLDw7h3ca",
        "outputId": "87a4cd6b-9c55-4784-8ded-d28f466de3e2"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[('scale',\n",
              "                                        ColumnTransformer(remainder='passthrough',\n",
              "                                                          transformers=[('scale',\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         ['age',\n",
              "                                                                          'creatine_phosphokinase',\n",
              "                                                                          'ejection_fraction',\n",
              "                                                                          'platelets',\n",
              "                                                                          'serum_creatinine',\n",
              "                                                                          'serum_sodium'])])),\n",
              "                                       ('logistic', LogisticRegression())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'logistic__C': [0.001, 0.01, 0.1, 0.5, 1, 10, 25]},\n",
              "             refit='accuracy',\n",
              "             scoring={'accuracy': make_scorer(accuracy_score),\n",
              "                      'f1': make_scorer(f1_score),\n",
              "                      'precision': make_scorer(precision_score, zero_division=1),\n",
              "                      'recall': make_scorer(recall_score),\n",
              "                      'roc_auc': make_scorer(roc_auc_score, needs_proba=True)})"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scale&#x27;,\n",
              "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                          transformers=[(&#x27;scale&#x27;,\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         [&#x27;age&#x27;,\n",
              "                                                                          &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                          &#x27;ejection_fraction&#x27;,\n",
              "                                                                          &#x27;platelets&#x27;,\n",
              "                                                                          &#x27;serum_creatinine&#x27;,\n",
              "                                                                          &#x27;serum_sodium&#x27;])])),\n",
              "                                       (&#x27;logistic&#x27;, LogisticRegression())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;logistic__C&#x27;: [0.001, 0.01, 0.1, 0.5, 1, 10, 25]},\n",
              "             refit=&#x27;accuracy&#x27;,\n",
              "             scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                      &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                      &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                      &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                      &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scale&#x27;,\n",
              "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                          transformers=[(&#x27;scale&#x27;,\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         [&#x27;age&#x27;,\n",
              "                                                                          &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                          &#x27;ejection_fraction&#x27;,\n",
              "                                                                          &#x27;platelets&#x27;,\n",
              "                                                                          &#x27;serum_creatinine&#x27;,\n",
              "                                                                          &#x27;serum_sodium&#x27;])])),\n",
              "                                       (&#x27;logistic&#x27;, LogisticRegression())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;logistic__C&#x27;: [0.001, 0.01, 0.1, 0.5, 1, 10, 25]},\n",
              "             refit=&#x27;accuracy&#x27;,\n",
              "             scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                      &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                      &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                      &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                      &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scale&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;age&#x27;,\n",
              "                                                   &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                   &#x27;ejection_fraction&#x27;,\n",
              "                                                   &#x27;platelets&#x27;,\n",
              "                                                   &#x27;serum_creatinine&#x27;,\n",
              "                                                   &#x27;serum_sodium&#x27;])])),\n",
              "                (&#x27;logistic&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scale: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                 [&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;,\n",
              "                                  &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;,\n",
              "                                  &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scale</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;, &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;, &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_regression_hyperparameter_table = [\n",
        "    [\"Hyperparameter\", \"Value\"]\n",
        "]\n",
        "\n",
        "for key, val in logistic_grid_search.best_params_.items():\n",
        "  log_regression_hyperparameter_table.append([key.replace('logistic__', ''), val])\n",
        "\n",
        "create_table(log_regression_hyperparameter_table, 'Best Logistic Regression Model Hyperparameters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "SJotF3vH6rev",
        "outputId": "4f94735d-7944-4037-e7ec-208d5f00fa47"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Training Set"
      ],
      "metadata": {
        "id": "cS4PP0q36xQA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_regression_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "best_model_indx = logistic_grid_search.best_index_\n",
        "scoring_types = ['accuracy', 'recall', 'precision', 'f1', 'roc_auc']\n",
        "model_data = []\n",
        "\n",
        "for st in scoring_types:\n",
        "  model_data.append(round(logistic_grid_search.cv_results_[f'mean_test_{st}'][best_model_indx], 3))\n",
        "\n",
        "log_regression_metrics_table.append(model_data)\n",
        "\n",
        "create_table(log_regression_metrics_table, 'Best Logistic Regression Model Mean Performance Metrics \\n On Training and Validation Datasets')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "P2u7d17k-3UO",
        "outputId": "82d4c7ba-9346-4eb9-9ee8-f0f8fa5e25b8"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Testing Set"
      ],
      "metadata": {
        "id": "J1mYNlCX65_W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_y_pred = logistic_grid_search.predict(X_test)\n",
        "\n",
        "log_test_metrics = create_test_metrics_row(y_test, log_y_pred, 'Logistic Regression')\n",
        "\n",
        "test_metrics_data.append(log_test_metrics)"
      ],
      "metadata": {
        "id": "pH_5WIib65_X"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log_test_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "log_test_metrics_table.append(log_test_metrics[:len(log_test_metrics) - 1])\n",
        "\n",
        "create_table(log_test_metrics_table, 'Best Logistic Regression Model Performance Metrics \\n On Test Dataset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "h-nzOdrsBT5K",
        "outputId": "299f9390-484d-4071-b4fb-459bc9a93270"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['class 0: Patient Not Deceased', 'class 1: Patient Deceased']\n",
        "print(classification_report(y_test, log_y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvX1srCZ-OGg",
        "outputId": "b55d3579-40b0-4e28-934a-5dc9247e26f9"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               precision    recall  f1-score   support\n",
            "\n",
            "class 0: Patient Not Deceased       0.85      0.94      0.90        50\n",
            "    class 1: Patient Deceased       0.75      0.53      0.62        17\n",
            "\n",
            "                     accuracy                           0.84        67\n",
            "                    macro avg       0.80      0.73      0.76        67\n",
            "                 weighted avg       0.83      0.84      0.83        67\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_test, log_y_pred, cmap=plt.cm.Blues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "f3ee2740-5f36-4bbb-c5b0-343887fa733e",
        "id": "E35aEZIW65_X"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4c8864cbe0>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RocCurveDisplay.from_predictions(y_test, log_y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "24ef1dd4-44df-4f47-ebb5-02bc0fab003a",
        "id": "PuDDspCc65_X"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f4c883fc160>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN"
      ],
      "metadata": {
        "id": "3LaVX0Hvi0Gj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning With StratifiedKFold and GridSearchCV"
      ],
      "metadata": {
        "id": "twSInTFbD0lS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn_pipeline = Pipeline([\n",
        "    ('scaler', standardizer),\n",
        "    ('knn', KNeighborsClassifier())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'knn__n_neighbors': [1, 3, 9, 17, 25, 50],\n",
        "}\n",
        "\n",
        "stratkf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "\n",
        "scoring = {\n",
        "        'accuracy': make_scorer(accuracy_score),\n",
        "        'recall': make_scorer(recall_score),\n",
        "        'precision': make_scorer(precision_score, zero_division=1),\n",
        "        'f1': make_scorer(f1_score),\n",
        "        'roc_auc': make_scorer(roc_auc_score, needs_proba=True)\n",
        "}\n",
        "\n",
        "knn_grid_search = GridSearchCV(knn_pipeline, param_grid, scoring=scoring, refit='accuracy', cv=stratkf, n_jobs=-1)\n",
        "knn_grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "DpRmtmuk9EsF",
        "outputId": "4dac8ac5-80d6-44f6-ab09-3672fdfd0852"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[('scaler',\n",
              "                                        ColumnTransformer(remainder='passthrough',\n",
              "                                                          transformers=[('scale',\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         ['age',\n",
              "                                                                          'creatine_phosphokinase',\n",
              "                                                                          'ejection_fraction',\n",
              "                                                                          'platelets',\n",
              "                                                                          'serum_creatinine',\n",
              "                                                                          'serum_sodium'])])),\n",
              "                                       ('knn', KNeighborsClassifier())]),\n",
              "             n_jobs=-1, param_grid={'knn__n_neighbors': [1, 3, 9, 17, 25, 50]},\n",
              "             refit='accuracy',\n",
              "             scoring={'accuracy': make_scorer(accuracy_score),\n",
              "                      'f1': make_scorer(f1_score),\n",
              "                      'precision': make_scorer(precision_score, zero_division=1),\n",
              "                      'recall': make_scorer(recall_score),\n",
              "                      'roc_auc': make_scorer(roc_auc_score, needs_proba=True)})"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                          transformers=[(&#x27;scale&#x27;,\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         [&#x27;age&#x27;,\n",
              "                                                                          &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                          &#x27;ejection_fraction&#x27;,\n",
              "                                                                          &#x27;platelets&#x27;,\n",
              "                                                                          &#x27;serum_creatinine&#x27;,\n",
              "                                                                          &#x27;serum_sodium&#x27;])])),\n",
              "                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
              "             n_jobs=-1, param_grid={&#x27;knn__n_neighbors&#x27;: [1, 3, 9, 17, 25, 50]},\n",
              "             refit=&#x27;accuracy&#x27;,\n",
              "             scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                      &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                      &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                      &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                      &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                          transformers=[(&#x27;scale&#x27;,\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         [&#x27;age&#x27;,\n",
              "                                                                          &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                          &#x27;ejection_fraction&#x27;,\n",
              "                                                                          &#x27;platelets&#x27;,\n",
              "                                                                          &#x27;serum_creatinine&#x27;,\n",
              "                                                                          &#x27;serum_sodium&#x27;])])),\n",
              "                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n",
              "             n_jobs=-1, param_grid={&#x27;knn__n_neighbors&#x27;: [1, 3, 9, 17, 25, 50]},\n",
              "             refit=&#x27;accuracy&#x27;,\n",
              "             scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                      &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                      &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                      &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                      &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;age&#x27;,\n",
              "                                                   &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                   &#x27;ejection_fraction&#x27;,\n",
              "                                                   &#x27;platelets&#x27;,\n",
              "                                                   &#x27;serum_creatinine&#x27;,\n",
              "                                                   &#x27;serum_sodium&#x27;])])),\n",
              "                (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaler: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                 [&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;,\n",
              "                                  &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;,\n",
              "                                  &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scale</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;, &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;, &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_hyperparameter_table = [\n",
        "    [\"Hyperparameter\", \"Value\"]\n",
        "]\n",
        "\n",
        "for key, val in knn_grid_search.best_params_.items():\n",
        "  knn_hyperparameter_table.append([key.replace('knn__', ''), val])\n",
        "\n",
        "create_table(knn_hyperparameter_table, 'Best Logistic Regression Model Hyperparameters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "mPDr-XlIafYU",
        "outputId": "82702144-6ce3-4a4a-c896-fc0fee0ebe56"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Training Set"
      ],
      "metadata": {
        "id": "tDwnWfWUeSl8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn_training_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "best_model_indx = knn_grid_search.best_index_\n",
        "scoring_types = ['accuracy', 'recall', 'precision', 'f1', 'roc_auc']\n",
        "model_data = []\n",
        "\n",
        "for st in scoring_types:\n",
        "  model_data.append(round(knn_grid_search.cv_results_[f'mean_test_{st}'][best_model_indx], 3))\n",
        "\n",
        "knn_training_metrics_table.append(model_data)\n",
        "\n",
        "create_table(knn_training_metrics_table, 'Best KNN Model Mean Performance Metrics \\n On Training and Validation Datasets')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "OveSSUT_NSgK",
        "outputId": "52b81fca-4955-4a3e-bb5f-b58b7259b7a6"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Test Set"
      ],
      "metadata": {
        "id": "cKUWhfPswrBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn_y_pred = knn_grid_search.predict(X_test)\n",
        "\n",
        "knn_test_metrics = create_test_metrics_row(y_test, knn_y_pred, 'KNN')\n",
        "\n",
        "test_metrics_data.append(knn_test_metrics)"
      ],
      "metadata": {
        "id": "btFZqG8ad5eV"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_test_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "knn_test_metrics_table.append(knn_test_metrics[:len(knn_test_metrics) - 1])\n",
        "\n",
        "create_table(knn_test_metrics_table, 'Best KNN Model Performance Metrics \\n On Test Dataset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "7XUEWEQwAczo",
        "outputId": "69e78301-eb17-4657-dc9e-001fe5e2397e"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['class 0: Patient Not Deceased', 'class 1: Patient Deceased']\n",
        "print(classification_report(y_test, knn_y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-RFaoYbGfmB",
        "outputId": "66a9e4fd-6efc-4f8e-dcd1-ba8b371a42f1"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               precision    recall  f1-score   support\n",
            "\n",
            "class 0: Patient Not Deceased       0.85      0.92      0.88        50\n",
            "    class 1: Patient Deceased       0.69      0.53      0.60        17\n",
            "\n",
            "                     accuracy                           0.82        67\n",
            "                    macro avg       0.77      0.72      0.74        67\n",
            "                 weighted avg       0.81      0.82      0.81        67\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_test, knn_y_pred, cmap=plt.cm.Blues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "G3dzunyfeEGn",
        "outputId": "275c7ecd-5a51-442c-f945-507e734d1474"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4c8a998ee0>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RocCurveDisplay.from_predictions(y_test, knn_y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "IFcD4UjEeJfo",
        "outputId": "7a73b17e-085b-4b1b-db70-048f60e99d00"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f4c880b7310>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest Classifier"
      ],
      "metadata": {
        "id": "o8QxBUe-i3Sn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning With Stratified K-Fold CV and Grid Search CV"
      ],
      "metadata": {
        "id": "K4FQz6wGMPRc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest_pipeline = Pipeline([\n",
        "    ('scaler', standardizer),\n",
        "    ('random_forest', RandomForestClassifier())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'random_forest__n_estimators': [25, 50, 100, 125, 150, 200],\n",
        "    'random_forest__max_depth': [None, 5, 10, 25],\n",
        "    'random_forest__min_samples_split': [2, 5, 10],\n",
        "    'random_forest__min_samples_leaf': [1, 2, 5],\n",
        "    'random_forest__max_features': ['sqrt', 'log2']\n",
        "}\n",
        "\n",
        "stratkf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "\n",
        "scoring = {\n",
        "        'accuracy': make_scorer(accuracy_score),\n",
        "        'recall': make_scorer(recall_score),\n",
        "        'precision': make_scorer(precision_score, zero_division=1),\n",
        "        'f1': make_scorer(f1_score),\n",
        "        'roc_auc': make_scorer(roc_auc_score, needs_proba=True)\n",
        "}\n",
        "\n",
        "random_forest_rs_search = RandomizedSearchCV(random_forest_pipeline, param_distributions=param_grid, scoring=scoring, refit='accuracy', cv=stratkf, n_jobs=-1)\n",
        "random_forest_rs_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "pyhea3dD-1W4",
        "outputId": "7bc7a4a2-5ad2-4a85-86a3-59c4af2ee833"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "                   estimator=Pipeline(steps=[('scaler',\n",
              "                                              ColumnTransformer(remainder='passthrough',\n",
              "                                                                transformers=[('scale',\n",
              "                                                                               StandardScaler(),\n",
              "                                                                               ['age',\n",
              "                                                                                'creatine_phosphokinase',\n",
              "                                                                                'ejection_fraction',\n",
              "                                                                                'platelets',\n",
              "                                                                                'serum_creatinine',\n",
              "                                                                                'serum_sodium'])])),\n",
              "                                             ('random_forest',\n",
              "                                              RandomForestClassifier())]),\n",
              "                   n_jo...\n",
              "                                        'random_forest__min_samples_leaf': [1,\n",
              "                                                                            2,\n",
              "                                                                            5],\n",
              "                                        'random_forest__min_samples_split': [2,\n",
              "                                                                             5,\n",
              "                                                                             10],\n",
              "                                        'random_forest__n_estimators': [25, 50,\n",
              "                                                                        100,\n",
              "                                                                        125,\n",
              "                                                                        150,\n",
              "                                                                        200]},\n",
              "                   refit='accuracy',\n",
              "                   scoring={'accuracy': make_scorer(accuracy_score),\n",
              "                            'f1': make_scorer(f1_score),\n",
              "                            'precision': make_scorer(precision_score, zero_division=1),\n",
              "                            'recall': make_scorer(recall_score),\n",
              "                            'roc_auc': make_scorer(roc_auc_score, needs_proba=True)})"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "                   estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                              ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                                transformers=[(&#x27;scale&#x27;,\n",
              "                                                                               StandardScaler(),\n",
              "                                                                               [&#x27;age&#x27;,\n",
              "                                                                                &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                                &#x27;ejection_fraction&#x27;,\n",
              "                                                                                &#x27;platelets&#x27;,\n",
              "                                                                                &#x27;serum_creatinine&#x27;,\n",
              "                                                                                &#x27;serum_sodium&#x27;])])),\n",
              "                                             (&#x27;random_forest&#x27;,\n",
              "                                              RandomForestClassifier())]),\n",
              "                   n_jo...\n",
              "                                        &#x27;random_forest__min_samples_leaf&#x27;: [1,\n",
              "                                                                            2,\n",
              "                                                                            5],\n",
              "                                        &#x27;random_forest__min_samples_split&#x27;: [2,\n",
              "                                                                             5,\n",
              "                                                                             10],\n",
              "                                        &#x27;random_forest__n_estimators&#x27;: [25, 50,\n",
              "                                                                        100,\n",
              "                                                                        125,\n",
              "                                                                        150,\n",
              "                                                                        200]},\n",
              "                   refit=&#x27;accuracy&#x27;,\n",
              "                   scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                            &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                            &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                            &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                            &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "                   estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                              ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                                transformers=[(&#x27;scale&#x27;,\n",
              "                                                                               StandardScaler(),\n",
              "                                                                               [&#x27;age&#x27;,\n",
              "                                                                                &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                                &#x27;ejection_fraction&#x27;,\n",
              "                                                                                &#x27;platelets&#x27;,\n",
              "                                                                                &#x27;serum_creatinine&#x27;,\n",
              "                                                                                &#x27;serum_sodium&#x27;])])),\n",
              "                                             (&#x27;random_forest&#x27;,\n",
              "                                              RandomForestClassifier())]),\n",
              "                   n_jo...\n",
              "                                        &#x27;random_forest__min_samples_leaf&#x27;: [1,\n",
              "                                                                            2,\n",
              "                                                                            5],\n",
              "                                        &#x27;random_forest__min_samples_split&#x27;: [2,\n",
              "                                                                             5,\n",
              "                                                                             10],\n",
              "                                        &#x27;random_forest__n_estimators&#x27;: [25, 50,\n",
              "                                                                        100,\n",
              "                                                                        125,\n",
              "                                                                        150,\n",
              "                                                                        200]},\n",
              "                   refit=&#x27;accuracy&#x27;,\n",
              "                   scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                            &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                            &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                            &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                            &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;age&#x27;,\n",
              "                                                   &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                   &#x27;ejection_fraction&#x27;,\n",
              "                                                   &#x27;platelets&#x27;,\n",
              "                                                   &#x27;serum_creatinine&#x27;,\n",
              "                                                   &#x27;serum_sodium&#x27;])])),\n",
              "                (&#x27;random_forest&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaler: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                 [&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;,\n",
              "                                  &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;,\n",
              "                                  &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scale</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;, &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;, &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest_param_table = [\n",
        "    [\"Hyperparameter\", \"Value\"]\n",
        "]\n",
        "\n",
        "for key, val in random_forest_rs_search.best_params_.items():\n",
        "  random_forest_param_table.append([key.replace('random_forest__', ''), val])\n",
        "\n",
        "create_table(random_forest_param_table, 'Best Random Forest Classifier Model Hyperparameters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "6tye1iKGPuPQ",
        "outputId": "0696900e-4a38-4913-f3da-e04b5114af04"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Training Set"
      ],
      "metadata": {
        "id": "bAVSc1JT4mZ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "best_model_indx = random_forest_rs_search.best_index_\n",
        "scoring_types = ['accuracy', 'recall', 'precision', 'f1', 'roc_auc']\n",
        "model_data = []\n",
        "\n",
        "for st in scoring_types:\n",
        "  model_data.append(round(random_forest_rs_search.cv_results_[f'mean_test_{st}'][best_model_indx], 3))\n",
        "\n",
        "random_forest_metrics_table.append(model_data)\n",
        "\n",
        "create_table(random_forest_metrics_table, 'Best Random Forest Classifier Model Mean Performance Metrics \\n On Training and Validation Datasets')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Baa6kyWgQwSo",
        "outputId": "97a24860-d38e-407d-f4d8-ff54d63e561d"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Test Set"
      ],
      "metadata": {
        "id": "oScYKufs32j1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_y_pred = random_forest_rs_search.predict(X_test)\n",
        "\n",
        "rf_test_metrics = create_test_metrics_row(y_test, rf_y_pred, 'Random Forest Classifier')\n",
        "\n",
        "test_metrics_data.append(rf_test_metrics)"
      ],
      "metadata": {
        "id": "tYwg8aqPGLzE"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_test_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "rf_test_metrics_table.append(rf_test_metrics[:len(rf_test_metrics) - 1])\n",
        "\n",
        "create_table(rf_test_metrics_table, 'Best Random Forest Classifier Model Performance Metrics \\n On Test Dataset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "VRDIs0xqBmry",
        "outputId": "eee97415-fca9-4bd5-8538-96df3ec55167"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['class 0: Patient Not Deceased', 'class 1: Patient Deceased']\n",
        "print(classification_report(y_test, rf_y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgE_6XZVHkqk",
        "outputId": "3ab621d1-9138-4806-a68c-ad73f920f35c"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               precision    recall  f1-score   support\n",
            "\n",
            "class 0: Patient Not Deceased       0.85      0.92      0.88        50\n",
            "    class 1: Patient Deceased       0.69      0.53      0.60        17\n",
            "\n",
            "                     accuracy                           0.82        67\n",
            "                    macro avg       0.77      0.72      0.74        67\n",
            "                 weighted avg       0.81      0.82      0.81        67\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_test, rf_y_pred, cmap=plt.cm.Blues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "qV3_P62oGVsq",
        "outputId": "644fea4e-cd70-4f29-cbd8-4ef3cded709e"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4c884dc2b0>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RocCurveDisplay.from_predictions(y_test, rf_y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "i2HcadRjGYLo",
        "outputId": "d23d9c61-2eda-44d4-8212-0ef92e746516"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f4c88728c40>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gradient Boosted Trees Classifier"
      ],
      "metadata": {
        "id": "B2C37UNPjFgL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning With Stratified K-Fold CV and Grid Search CV"
      ],
      "metadata": {
        "id": "Vizosw0kMER2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_pipeline = Pipeline([\n",
        "    ('scaler', standardizer),\n",
        "    ('gbc', GradientBoostingClassifier())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'gbc__n_estimators': [100, 200, 300],\n",
        "    'gbc__learning_rate': [0.01, 0.1, 1.0],\n",
        "    'gbc__max_depth': [3, 4, 5],\n",
        "    'gbc__min_samples_split': [2, 3, 4],\n",
        "    'gbc__min_samples_leaf': [1, 2, 3],\n",
        "    'gbc__subsample': [0.5, 0.75, 1.0],\n",
        "    'gbc__max_features': ['sqrt', 'log2', None]\n",
        "}\n",
        "\n",
        "stratkf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "\n",
        "scoring = {\n",
        "        'accuracy': make_scorer(accuracy_score),\n",
        "        'recall': make_scorer(recall_score),\n",
        "        'precision': make_scorer(precision_score, zero_division=1),\n",
        "        'f1': make_scorer(f1_score),\n",
        "        'roc_auc': make_scorer(roc_auc_score, needs_proba=True)\n",
        "}\n",
        "\n",
        "gbc_rs_search = RandomizedSearchCV(gbc_pipeline, param_distributions=param_grid, scoring=scoring, refit='accuracy', cv=stratkf, n_jobs=-1)\n",
        "gbc_rs_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "ORq92tuWkRE7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "a23c543a-a295-4ac3-ec8d-f70ce95367b2"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "                   estimator=Pipeline(steps=[('scaler',\n",
              "                                              ColumnTransformer(remainder='passthrough',\n",
              "                                                                transformers=[('scale',\n",
              "                                                                               StandardScaler(),\n",
              "                                                                               ['age',\n",
              "                                                                                'creatine_phosphokinase',\n",
              "                                                                                'ejection_fraction',\n",
              "                                                                                'platelets',\n",
              "                                                                                'serum_creatinine',\n",
              "                                                                                'serum_sodium'])])),\n",
              "                                             ('gbc',\n",
              "                                              GradientBoostingClassifier())]),\n",
              "                   n_jobs=-1,...\n",
              "                                        'gbc__min_samples_leaf': [1, 2, 3],\n",
              "                                        'gbc__min_samples_split': [2, 3, 4],\n",
              "                                        'gbc__n_estimators': [100, 200, 300],\n",
              "                                        'gbc__subsample': [0.5, 0.75, 1.0]},\n",
              "                   refit='accuracy',\n",
              "                   scoring={'accuracy': make_scorer(accuracy_score),\n",
              "                            'f1': make_scorer(f1_score),\n",
              "                            'precision': make_scorer(precision_score, zero_division=1),\n",
              "                            'recall': make_scorer(recall_score),\n",
              "                            'roc_auc': make_scorer(roc_auc_score, needs_proba=True)})"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "                   estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                              ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                                transformers=[(&#x27;scale&#x27;,\n",
              "                                                                               StandardScaler(),\n",
              "                                                                               [&#x27;age&#x27;,\n",
              "                                                                                &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                                &#x27;ejection_fraction&#x27;,\n",
              "                                                                                &#x27;platelets&#x27;,\n",
              "                                                                                &#x27;serum_creatinine&#x27;,\n",
              "                                                                                &#x27;serum_sodium&#x27;])])),\n",
              "                                             (&#x27;gbc&#x27;,\n",
              "                                              GradientBoostingClassifier())]),\n",
              "                   n_jobs=-1,...\n",
              "                                        &#x27;gbc__min_samples_leaf&#x27;: [1, 2, 3],\n",
              "                                        &#x27;gbc__min_samples_split&#x27;: [2, 3, 4],\n",
              "                                        &#x27;gbc__n_estimators&#x27;: [100, 200, 300],\n",
              "                                        &#x27;gbc__subsample&#x27;: [0.5, 0.75, 1.0]},\n",
              "                   refit=&#x27;accuracy&#x27;,\n",
              "                   scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                            &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                            &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                            &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                            &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "                   estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                              ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                                transformers=[(&#x27;scale&#x27;,\n",
              "                                                                               StandardScaler(),\n",
              "                                                                               [&#x27;age&#x27;,\n",
              "                                                                                &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                                &#x27;ejection_fraction&#x27;,\n",
              "                                                                                &#x27;platelets&#x27;,\n",
              "                                                                                &#x27;serum_creatinine&#x27;,\n",
              "                                                                                &#x27;serum_sodium&#x27;])])),\n",
              "                                             (&#x27;gbc&#x27;,\n",
              "                                              GradientBoostingClassifier())]),\n",
              "                   n_jobs=-1,...\n",
              "                                        &#x27;gbc__min_samples_leaf&#x27;: [1, 2, 3],\n",
              "                                        &#x27;gbc__min_samples_split&#x27;: [2, 3, 4],\n",
              "                                        &#x27;gbc__n_estimators&#x27;: [100, 200, 300],\n",
              "                                        &#x27;gbc__subsample&#x27;: [0.5, 0.75, 1.0]},\n",
              "                   refit=&#x27;accuracy&#x27;,\n",
              "                   scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                            &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                            &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                            &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                            &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;age&#x27;,\n",
              "                                                   &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                   &#x27;ejection_fraction&#x27;,\n",
              "                                                   &#x27;platelets&#x27;,\n",
              "                                                   &#x27;serum_creatinine&#x27;,\n",
              "                                                   &#x27;serum_sodium&#x27;])])),\n",
              "                (&#x27;gbc&#x27;, GradientBoostingClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaler: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                 [&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;,\n",
              "                                  &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;,\n",
              "                                  &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scale</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;, &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;, &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_param_table = [\n",
        "    [\"Hyperparameter\", \"Value\"]\n",
        "]\n",
        "\n",
        "for key, val in gbc_rs_search.best_params_.items():\n",
        "  gbc_param_table.append([key.replace('gbc__', ''), val])\n",
        "\n",
        "create_table(gbc_param_table, 'Best Gradient Boosted Trees Model Hyperparameters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "ml-MZ1ppRLWT",
        "outputId": "375de90e-8e9a-442f-f7ce-9820764e787b"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores Training and Validation Sets With Cross Validation"
      ],
      "metadata": {
        "id": "VBBYnl6ELyIW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "best_model_indx = gbc_rs_search.best_index_\n",
        "scoring_types = ['accuracy', 'recall', 'precision', 'f1', 'roc_auc']\n",
        "model_data = []\n",
        "\n",
        "for st in scoring_types:\n",
        "  model_data.append(round(gbc_rs_search.cv_results_[f'mean_test_{st}'][best_model_indx], 3))\n",
        "\n",
        "gbc_metrics_table.append(model_data)\n",
        "\n",
        "create_table(gbc_metrics_table, 'Best Gradient Boosted Tree Mean Performance Metrics \\n On Training and Validation Datasets')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ILVNTMWqRd9g",
        "outputId": "9689d774-2597-479e-8ac7-1b96d4c54c40"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Test Set"
      ],
      "metadata": {
        "id": "C0U5vd6L14BV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_y_pred = gbc_rs_search.predict(X_test)\n",
        "\n",
        "gbc_test_metrics = create_test_metrics_row(y_test, gbc_y_pred, 'Gradient Boosted Tree')\n",
        "\n",
        "test_metrics_data.append(gbc_test_metrics)"
      ],
      "metadata": {
        "id": "wbCjx7xm14Bb"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gbc_test_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "gbc_test_metrics_table.append(gbc_test_metrics[:len(gbc_test_metrics) - 1])\n",
        "\n",
        "create_table(gbc_test_metrics_table, 'Best Gradient Boosted Tress Classifier Model Performance Metrics \\n On Test Dataset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "fw5bTGXqB3_e",
        "outputId": "4a590cc8-41d2-459e-f242-a7adef6c55a5"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['class 0: Patient Not Deceased', 'class 1: Patient Deceased']\n",
        "print(classification_report(y_test, gbc_y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBCwEawcHr4L",
        "outputId": "e95c80cd-9e18-4f5a-a6f9-2c0f99c4f577"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               precision    recall  f1-score   support\n",
            "\n",
            "class 0: Patient Not Deceased       0.87      0.92      0.89        50\n",
            "    class 1: Patient Deceased       0.71      0.59      0.65        17\n",
            "\n",
            "                     accuracy                           0.84        67\n",
            "                    macro avg       0.79      0.75      0.77        67\n",
            "                 weighted avg       0.83      0.84      0.83        67\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_test, gbc_y_pred, cmap=plt.cm.Blues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "2649f755-b09b-4495-eb85-5ef60a00bd07",
        "id": "Iagb-fua14Bc"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4c8253f9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RocCurveDisplay.from_predictions(y_test, gbc_y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "40c8582e-27bf-4c26-d270-216b5a72dc08",
        "id": "QaOkSs1W14Bc"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f4c8257af50>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# XGBoost"
      ],
      "metadata": {
        "id": "Rdgtcyh6ChpV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_pipeline = Pipeline([\n",
        "    ('scaler', standardizer),\n",
        "    ('xgb', XGBClassifier())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'xgb__n_estimators': [50, 100, 200],\n",
        "    'xgb__learning_rate': [0.01, 0.1, 1.0],\n",
        "    'xgb__max_depth': [3, 4, 5],\n",
        "    'xgb__subsample': [0.5, 0.75, 1.0],\n",
        "}\n",
        "\n",
        "stratkf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "\n",
        "scoring = {\n",
        "        'accuracy': make_scorer(accuracy_score),\n",
        "        'recall': make_scorer(recall_score),\n",
        "        'precision': make_scorer(precision_score, zero_division=1),\n",
        "        'f1': make_scorer(f1_score),\n",
        "        'roc_auc': make_scorer(roc_auc_score, needs_proba=True)\n",
        "}\n",
        "\n",
        "xgb_rs_search = GridSearchCV(xgb_pipeline, param_grid, scoring=scoring, refit='accuracy', cv=stratkf, n_jobs=-1)\n",
        "# xgb_rs_search = RandomizedSearchCV(xgb_pipeline, param_distributions=param_grid, scoring=scoring, refit='precision', cv=stratkf, n_jobs=-1)\n",
        "xgb_rs_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "hsYlzEbDChYt",
        "outputId": "2449856d-b2d2-4b6f-db35-7f73543d846b"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[('scaler',\n",
              "                                        ColumnTransformer(remainder='passthrough',\n",
              "                                                          transformers=[('scale',\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         ['age',\n",
              "                                                                          'creatine_phosphokinase',\n",
              "                                                                          'ejection_fraction',\n",
              "                                                                          'platelets',\n",
              "                                                                          'serum_creatinine',\n",
              "                                                                          'serum_sodium'])])),\n",
              "                                       ('xgb',\n",
              "                                        XGBClassifier(base_score=None,\n",
              "                                                      booster=None,\n",
              "                                                      callb...\n",
              "             param_grid={'xgb__learning_rate': [0.01, 0.1, 1.0],\n",
              "                         'xgb__max_depth': [3, 4, 5],\n",
              "                         'xgb__n_estimators': [50, 100, 200],\n",
              "                         'xgb__subsample': [0.5, 0.75, 1.0]},\n",
              "             refit='accuracy',\n",
              "             scoring={'accuracy': make_scorer(accuracy_score),\n",
              "                      'f1': make_scorer(f1_score),\n",
              "                      'precision': make_scorer(precision_score, zero_division=1),\n",
              "                      'recall': make_scorer(recall_score),\n",
              "                      'roc_auc': make_scorer(roc_auc_score, needs_proba=True)})"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                          transformers=[(&#x27;scale&#x27;,\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         [&#x27;age&#x27;,\n",
              "                                                                          &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                          &#x27;ejection_fraction&#x27;,\n",
              "                                                                          &#x27;platelets&#x27;,\n",
              "                                                                          &#x27;serum_creatinine&#x27;,\n",
              "                                                                          &#x27;serum_sodium&#x27;])])),\n",
              "                                       (&#x27;xgb&#x27;,\n",
              "                                        XGBClassifier(base_score=None,\n",
              "                                                      booster=None,\n",
              "                                                      callb...\n",
              "             param_grid={&#x27;xgb__learning_rate&#x27;: [0.01, 0.1, 1.0],\n",
              "                         &#x27;xgb__max_depth&#x27;: [3, 4, 5],\n",
              "                         &#x27;xgb__n_estimators&#x27;: [50, 100, 200],\n",
              "                         &#x27;xgb__subsample&#x27;: [0.5, 0.75, 1.0]},\n",
              "             refit=&#x27;accuracy&#x27;,\n",
              "             scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                      &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                      &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                      &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                      &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-121\" type=\"checkbox\" ><label for=\"sk-estimator-id-121\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                                          transformers=[(&#x27;scale&#x27;,\n",
              "                                                                         StandardScaler(),\n",
              "                                                                         [&#x27;age&#x27;,\n",
              "                                                                          &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                                          &#x27;ejection_fraction&#x27;,\n",
              "                                                                          &#x27;platelets&#x27;,\n",
              "                                                                          &#x27;serum_creatinine&#x27;,\n",
              "                                                                          &#x27;serum_sodium&#x27;])])),\n",
              "                                       (&#x27;xgb&#x27;,\n",
              "                                        XGBClassifier(base_score=None,\n",
              "                                                      booster=None,\n",
              "                                                      callb...\n",
              "             param_grid={&#x27;xgb__learning_rate&#x27;: [0.01, 0.1, 1.0],\n",
              "                         &#x27;xgb__max_depth&#x27;: [3, 4, 5],\n",
              "                         &#x27;xgb__n_estimators&#x27;: [50, 100, 200],\n",
              "                         &#x27;xgb__subsample&#x27;: [0.5, 0.75, 1.0]},\n",
              "             refit=&#x27;accuracy&#x27;,\n",
              "             scoring={&#x27;accuracy&#x27;: make_scorer(accuracy_score),\n",
              "                      &#x27;f1&#x27;: make_scorer(f1_score),\n",
              "                      &#x27;precision&#x27;: make_scorer(precision_score, zero_division=1),\n",
              "                      &#x27;recall&#x27;: make_scorer(recall_score),\n",
              "                      &#x27;roc_auc&#x27;: make_scorer(roc_auc_score, needs_proba=True)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-122\" type=\"checkbox\" ><label for=\"sk-estimator-id-122\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;age&#x27;,\n",
              "                                                   &#x27;creatine_phosphokinase&#x27;,\n",
              "                                                   &#x27;ejection_fraction&#x27;,\n",
              "                                                   &#x27;platelets&#x27;,\n",
              "                                                   &#x27;serum_creatinine&#x27;,\n",
              "                                                   &#x27;serum_sodium&#x27;])])),\n",
              "                (&#x27;xgb&#x27;,\n",
              "                 XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "                               colsample_bylevel=None, colsample_bynode=None,\n",
              "                               colsample_bytree=None, device=...\n",
              "                               feature_types=None, gamma=None, grow_policy=None,\n",
              "                               importance_type=None,\n",
              "                               interaction_constraints=None, learning_rate=None,\n",
              "                               max_bin=None, max_cat_threshold=None,\n",
              "                               max_cat_to_onehot=None, max_delta_step=None,\n",
              "                               max_depth=None, max_leaves=None,\n",
              "                               min_child_weight=None, missing=nan,\n",
              "                               monotone_constraints=None, multi_strategy=None,\n",
              "                               n_estimators=None, n_jobs=None,\n",
              "                               num_parallel_tree=None, random_state=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-123\" type=\"checkbox\" ><label for=\"sk-estimator-id-123\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaler: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;scale&#x27;, StandardScaler(),\n",
              "                                 [&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;,\n",
              "                                  &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;,\n",
              "                                  &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-124\" type=\"checkbox\" ><label for=\"sk-estimator-id-124\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scale</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;creatine_phosphokinase&#x27;, &#x27;ejection_fraction&#x27;, &#x27;platelets&#x27;, &#x27;serum_creatinine&#x27;, &#x27;serum_sodium&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-125\" type=\"checkbox\" ><label for=\"sk-estimator-id-125\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-126\" type=\"checkbox\" ><label for=\"sk-estimator-id-126\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-127\" type=\"checkbox\" ><label for=\"sk-estimator-id-127\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-128\" type=\"checkbox\" ><label for=\"sk-estimator-id-128\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning With Stratified K-Fold CV and Grid Search CV"
      ],
      "metadata": {
        "id": "UUcItjridNIl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_param_table = [\n",
        "    [\"Hyperparameter\", \"Value\"]\n",
        "]\n",
        "\n",
        "for key, val in xgb_rs_search.best_params_.items():\n",
        "  xgb_param_table.append([key.replace('xgb__', ''), val])\n",
        "\n",
        "create_table(xgb_param_table, 'Best Gradient Boosted Trees Model Hyperparameters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "s5N4TTwWG-i7",
        "outputId": "e8abf8c9-dc96-4448-de9c-9358b1accc09"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores Training and Validation Sets With Cross Validation"
      ],
      "metadata": {
        "id": "vPhKa3EoddjA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "best_model_indx = xgb_rs_search.best_index_\n",
        "scoring_types = ['accuracy', 'recall', 'precision', 'f1', 'roc_auc']\n",
        "model_data = []\n",
        "\n",
        "for st in scoring_types:\n",
        "  model_data.append(round(xgb_rs_search.cv_results_[f'mean_test_{st}'][best_model_indx], 3))\n",
        "\n",
        "xgb_metrics_table.append(model_data)\n",
        "\n",
        "create_table(xgb_metrics_table, 'Best XGB Model Mean Performance Metrics \\n On Training and Validation Datasets')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "zHI-CdYWHJK1",
        "outputId": "254b7af3-276f-4c8c-c4e3-aef0ab638705"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Metric Scores on Test Set"
      ],
      "metadata": {
        "id": "O4A65d7UdiM7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_y_pred = xgb_rs_search.predict(X_test)\n",
        "\n",
        "xgb_test_metrics = create_test_metrics_row(y_test, xgb_y_pred, 'XGBoost')\n",
        "\n",
        "test_metrics_data.append(xgb_test_metrics)"
      ],
      "metadata": {
        "id": "1xttQFjjHUz4"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_test_metrics_table = [\n",
        "    ['Accuracy', 'Precision', 'Recall', 'F1', 'ROC AUC'],\n",
        "]\n",
        "\n",
        "xgb_test_metrics_table.append(xgb_test_metrics[:len(xgb_test_metrics) - 1])\n",
        "\n",
        "create_table(xgb_test_metrics_table, 'Best XGBoost Classifier Model Performance Metrics \\n On Test Dataset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Li_9Em0LHX_2",
        "outputId": "0b92ab86-ff55-49ef-901c-681c4a98a795"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['class 0: Patient Not Deceased', 'class 1: Patient Deceased']\n",
        "print(classification_report(y_test, xgb_y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2aIn8mPHacV",
        "outputId": "2b150194-a7ea-40ba-8a5d-1d21c426ac31"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               precision    recall  f1-score   support\n",
            "\n",
            "class 0: Patient Not Deceased       0.88      0.90      0.89        50\n",
            "    class 1: Patient Deceased       0.69      0.65      0.67        17\n",
            "\n",
            "                     accuracy                           0.84        67\n",
            "                    macro avg       0.78      0.77      0.78        67\n",
            "                 weighted avg       0.83      0.84      0.83        67\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_predictions(y_test, xgb_y_pred, cmap=plt.cm.Blues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7tqyHNuvHcfQ",
        "outputId": "3a6a7e59-3294-407d-eb38-35f48de93bd4"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4c8270a500>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RocCurveDisplay.from_predictions(y_test, xgb_y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "fezm1l1OHfLS",
        "outputId": "b6958de1-8c46-4d93-d13a-2ac31319fce0"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f4c82a07430>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Test Metrics Comparison"
      ],
      "metadata": {
        "id": "ZCU9fc4BFv2e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_metrics_df = pd.DataFrame(test_metrics_data, columns=test_metrics_columns)\n",
        "test_metrics_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Un1Ldft1GINy",
        "outputId": "8b77ffe5-d2ca-45c7-ee60-a19cd3e3b25f"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Accuracy  Precision  Recall     F1  ROC AUC                     Model\n",
              "0     0.836      0.750   0.529  0.621    0.735       Logistic Regression\n",
              "1     0.821      0.692   0.529  0.600    0.725                       KNN\n",
              "2     0.821      0.692   0.529  0.600    0.725  Random Forest Classifier\n",
              "3     0.836      0.714   0.588  0.645    0.754     Gradient Boosted Tree\n",
              "4     0.836      0.688   0.647  0.667    0.774                   XGBoost"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e849c619-fa17-45a2-8a61-98651748a7af\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>Model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.836</td>\n",
              "      <td>0.750</td>\n",
              "      <td>0.529</td>\n",
              "      <td>0.621</td>\n",
              "      <td>0.735</td>\n",
              "      <td>Logistic Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.821</td>\n",
              "      <td>0.692</td>\n",
              "      <td>0.529</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.725</td>\n",
              "      <td>KNN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.821</td>\n",
              "      <td>0.692</td>\n",
              "      <td>0.529</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.725</td>\n",
              "      <td>Random Forest Classifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.836</td>\n",
              "      <td>0.714</td>\n",
              "      <td>0.588</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.754</td>\n",
              "      <td>Gradient Boosted Tree</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.836</td>\n",
              "      <td>0.688</td>\n",
              "      <td>0.647</td>\n",
              "      <td>0.667</td>\n",
              "      <td>0.774</td>\n",
              "      <td>XGBoost</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e849c619-fa17-45a2-8a61-98651748a7af')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-e849c619-fa17-45a2-8a61-98651748a7af button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e849c619-fa17-45a2-8a61-98651748a7af');\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7bd9411c-36ea-46f0-afd4-715d3b7f3e43\">\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bd9411c-36ea-46f0-afd4-715d3b7f3e43 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_84f97540-d30e-4a5d-91d8-ce6f443d56ce\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_metrics_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_84f97540-d30e-4a5d-91d8-ce6f443d56ce button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_metrics_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_metrics_df",
              "summary": "{\n  \"name\": \"test_metrics_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008215838362577499,\n        \"min\": 0.821,\n        \"max\": 0.836,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.821,\n          0.836\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026023066690918677,\n        \"min\": 0.688,\n        \"max\": 0.75,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.692,\n          0.688\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05277120426899503,\n        \"min\": 0.529,\n        \"max\": 0.647,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.529,\n          0.588\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0292284108360342,\n        \"min\": 0.6,\n        \"max\": 0.667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6,\n          0.667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021173096136370816,\n        \"min\": 0.725,\n        \"max\": 0.774,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.725,\n          0.774\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"KNN\",\n          \"XGBoost\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "source": [
        "sns.barplot(test_metrics_df, x='Accuracy', y='Model', hue='Model', palette=\"deep\", alpha=.8)\n",
        "plt.title('Accuracy Scores on Test Set')\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "DT0kBtSzIEUJ",
        "outputId": "f17e0572-9ca5-42f1-8e82-edd3e8f8f073"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(test_metrics_df, x='Precision', y='Model', hue='Model', palette=\"deep\", alpha=.8)\n",
        "plt.title('Precision Scores on Test Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "deGB7F1bLYBI",
        "outputId": "aacbf616-0a6c-4de0-fe33-a2a6ef1e3909"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(test_metrics_df, x='Recall', y='Model', hue='Model', palette=\"deep\", alpha=.8)\n",
        "plt.title('Recall Scores on Test Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "IedBP-E9Lx-H",
        "outputId": "81f6fded-5433-4d9f-c403-d23007deeade"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(test_metrics_df, x='F1', y='Model', hue='Model', palette=\"deep\", alpha=.8)\n",
        "plt.title('F1 Scores on Test Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7rLRZMYtL1V2",
        "outputId": "99c30960-1efa-4b0c-b9f8-58e3298e79bd"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(test_metrics_df, x='ROC AUC', y='Model', hue='Model', palette=\"deep\", alpha=.8)\n",
        "plt.title('ROC AUC Scores on Test Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "p6Hk2qE2L2mk",
        "outputId": "bdea9549-ec95-4716-f8f6-5a686cb8a721"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}