Pushing files to the repo-gabcares/RandomForestClassifier-Sepsis from the directory- ../models/huggingface/RandomForestClassifier/

README.md

@@ -75,22 +75,22 @@ widget:
 | Hyperparameter                                                               | Value                                                                                                                                           |
 |------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|
 | memory                                                                       |                                                                                                                                                 |
-| steps                                                                        | [('preprocessor', ColumnTransformer(transformers=[('numerical_pipeline',<br />                                 Pipeline(steps=[('log_transformations',<br />                                                  FunctionTransformer(func=<ufunc 'log1p'>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='median')),<br />                                                 ('scaler', RobustScaler())]),<br />                                 ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',<br />                                  'age']),<br />                                ('categorical_pipeline',<br />                                 Pipeline(steps=[('as_categorical',<br />                                                  FunctionTransformer(func=<function as_...<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['insurance']),<br />                                ('feature_creation_pipeline',<br />                                 Pipeline(steps=[('feature_creation',<br />                                                  FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='most_frequent')),<br />                                                 ('encoder',<br />                                                  OneHotEncoder(drop='first',<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['age'])])), ('feature-selection', SelectKBest(k='all',<br />            score_func=<function mutual_info_classif at 0x000001E7EDA4E480>)), ('classifier', RandomForestClassifier(n_jobs=-1, random_state=2024))]                                                                                                                                                 |
+| steps                                                                        | [('preprocessor', ColumnTransformer(transformers=[('numerical_pipeline',<br />                                 Pipeline(steps=[('log_transformations',<br />                                                  FunctionTransformer(func=<ufunc 'log1p'>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='median')),<br />                                                 ('scaler', RobustScaler())]),<br />                                 ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',<br />                                  'age']),<br />                                ('categorical_pipeline',<br />                                 Pipeline(steps=[('as_categorical',<br />                                                  FunctionTransformer(func=<function as_...<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['insurance']),<br />                                ('feature_creation_pipeline',<br />                                 Pipeline(steps=[('feature_creation',<br />                                                  FunctionTransformer(func=<function feature_creation at 0x0000025B889134C0>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='most_frequent')),<br />                                                 ('encoder',<br />                                                  OneHotEncoder(drop='first',<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['age'])])), ('feature-selection', SelectKBest(k='all',<br />            score_func=<function mutual_info_classif at 0x0000025B81CA7920>)), ('classifier', RandomForestClassifier(n_jobs=-1, random_state=2024))]                                                                                                                                                 |
 | verbose                                                                      | False                                                                                                                                           |
-| preprocessor                                                                 | ColumnTransformer(transformers=[('numerical_pipeline',<br />                                 Pipeline(steps=[('log_transformations',<br />                                                  FunctionTransformer(func=<ufunc 'log1p'>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='median')),<br />                                                 ('scaler', RobustScaler())]),<br />                                 ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',<br />                                  'age']),<br />                                ('categorical_pipeline',<br />                                 Pipeline(steps=[('as_categorical',<br />                                                  FunctionTransformer(func=<function as_...<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['insurance']),<br />                                ('feature_creation_pipeline',<br />                                 Pipeline(steps=[('feature_creation',<br />                                                  FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='most_frequent')),<br />                                                 ('encoder',<br />                                                  OneHotEncoder(drop='first',<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['age'])])                                                                                                                                                 |
-| feature-selection                                                            | SelectKBest(k='all',<br />            score_func=<function mutual_info_classif at 0x000001E7EDA4E480>)                                                                                                                                                 |
+| preprocessor                                                                 | ColumnTransformer(transformers=[('numerical_pipeline',<br />                                 Pipeline(steps=[('log_transformations',<br />                                                  FunctionTransformer(func=<ufunc 'log1p'>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='median')),<br />                                                 ('scaler', RobustScaler())]),<br />                                 ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',<br />                                  'age']),<br />                                ('categorical_pipeline',<br />                                 Pipeline(steps=[('as_categorical',<br />                                                  FunctionTransformer(func=<function as_...<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['insurance']),<br />                                ('feature_creation_pipeline',<br />                                 Pipeline(steps=[('feature_creation',<br />                                                  FunctionTransformer(func=<function feature_creation at 0x0000025B889134C0>)),<br />                                                 ('imputer',<br />                                                  SimpleImputer(strategy='most_frequent')),<br />                                                 ('encoder',<br />                                                  OneHotEncoder(drop='first',<br />                                                                handle_unknown='infrequent_if_exist',<br />                                                                sparse_output=False))]),<br />                                 ['age'])])                                                                                                                                                 |
+| feature-selection                                                            | SelectKBest(k='all',<br />            score_func=<function mutual_info_classif at 0x0000025B81CA7920>)                                                                                                                                                 |
 | classifier                                                                   | RandomForestClassifier(n_jobs=-1, random_state=2024)                                                                                            |
 | preprocessor__force_int_remainder_cols                                       | True                                                                                                                                            |
 | preprocessor__n_jobs                                                         |                                                                                                                                                 |
 | preprocessor__remainder                                                      | drop                                                                                                                                            |
 | preprocessor__sparse_threshold                                               | 0.3                                                                                                                                             |
 | preprocessor__transformer_weights                                            |                                                                                                                                                 |
-| preprocessor__transformers                                                   | [('numerical_pipeline', Pipeline(steps=[('log_transformations',<br />                 FunctionTransformer(func=<ufunc 'log1p'>)),<br />                ('imputer', SimpleImputer(strategy='median')),<br />                ('scaler', RobustScaler())]), ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']), ('categorical_pipeline', Pipeline(steps=[('as_categorical',<br />                 FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))]), ['insurance']), ('feature_creation_pipeline', Pipeline(steps=[('feature_creation',<br />                 FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))]), ['age'])]                                                                                                                                                 |
+| preprocessor__transformers                                                   | [('numerical_pipeline', Pipeline(steps=[('log_transformations',<br />                 FunctionTransformer(func=<ufunc 'log1p'>)),<br />                ('imputer', SimpleImputer(strategy='median')),<br />                ('scaler', RobustScaler())]), ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']), ('categorical_pipeline', Pipeline(steps=[('as_categorical',<br />                 FunctionTransformer(func=<function as_category at 0x0000025B88910220>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))]), ['insurance']), ('feature_creation_pipeline', Pipeline(steps=[('feature_creation',<br />                 FunctionTransformer(func=<function feature_creation at 0x0000025B889134C0>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))]), ['age'])]                                                                                                                                                 |
 | preprocessor__verbose                                                        | False                                                                                                                                           |
 | preprocessor__verbose_feature_names_out                                      | True                                                                                                                                            |
 | preprocessor__numerical_pipeline                                             | Pipeline(steps=[('log_transformations',<br />                 FunctionTransformer(func=<ufunc 'log1p'>)),<br />                ('imputer', SimpleImputer(strategy='median')),<br />                ('scaler', RobustScaler())])                                                                                                                                                 |
-| preprocessor__categorical_pipeline                                           | Pipeline(steps=[('as_categorical',<br />                 FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))])                                                                                                                                                 |
-| preprocessor__feature_creation_pipeline                                      | Pipeline(steps=[('feature_creation',<br />                 FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))])                                                                                                                                                 |
+| preprocessor__categorical_pipeline                                           | Pipeline(steps=[('as_categorical',<br />                 FunctionTransformer(func=<function as_category at 0x0000025B88910220>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))])                                                                                                                                                 |
+| preprocessor__feature_creation_pipeline                                      | Pipeline(steps=[('feature_creation',<br />                 FunctionTransformer(func=<function feature_creation at 0x0000025B889134C0>)),<br />                ('imputer', SimpleImputer(strategy='most_frequent')),<br />                ('encoder',<br />                 OneHotEncoder(drop='first',<br />                               handle_unknown='infrequent_if_exist',<br />                               sparse_output=False))])                                                                                                                                                 |
 | preprocessor__numerical_pipeline__memory                                     |                                                                                                                                                 |
 | preprocessor__numerical_pipeline__steps                                      | [('log_transformations', FunctionTransformer(func=<ufunc 'log1p'>)), ('imputer', SimpleImputer(strategy='median')), ('scaler', RobustScaler())] |
 | preprocessor__numerical_pipeline__verbose                                    | False                                                                                                                                           |
@@ -117,15 +117,15 @@ widget:
 | preprocessor__numerical_pipeline__scaler__with_centering                     | True                                                                                                                                            |
 | preprocessor__numerical_pipeline__scaler__with_scaling                       | True                                                                                                                                            |
 | preprocessor__categorical_pipeline__memory                                   |                                                                                                                                                 |
-| preprocessor__categorical_pipeline__steps                                    | [('as_categorical', FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br />              sparse_output=False))]                                                                                                                                                 |
+| preprocessor__categorical_pipeline__steps                                    | [('as_categorical', FunctionTransformer(func=<function as_category at 0x0000025B88910220>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br />              sparse_output=False))]                                                                                                                                                 |
 | preprocessor__categorical_pipeline__verbose                                  | False                                                                                                                                           |
-| preprocessor__categorical_pipeline__as_categorical                           | FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)                                                                          |
+| preprocessor__categorical_pipeline__as_categorical                           | FunctionTransformer(func=<function as_category at 0x0000025B88910220>)                                                                          |
 | preprocessor__categorical_pipeline__imputer                                  | SimpleImputer(strategy='most_frequent')                                                                                                         |
 | preprocessor__categorical_pipeline__encoder                                  | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br />              sparse_output=False)                                                                                                                                                 |
 | preprocessor__categorical_pipeline__as_categorical__accept_sparse            | False                                                                                                                                           |
 | preprocessor__categorical_pipeline__as_categorical__check_inverse            | True                                                                                                                                            |
 | preprocessor__categorical_pipeline__as_categorical__feature_names_out        |                                                                                                                                                 |
-| preprocessor__categorical_pipeline__as_categorical__func                     | <function as_category at 0x000001E7F1450680>                                                                                                    |
+| preprocessor__categorical_pipeline__as_categorical__func                     | <function as_category at 0x0000025B88910220>                                                                                                    |
 | preprocessor__categorical_pipeline__as_categorical__inv_kw_args              |                                                                                                                                                 |
 | preprocessor__categorical_pipeline__as_categorical__inverse_func             |                                                                                                                                                 |
 | preprocessor__categorical_pipeline__as_categorical__kw_args                  |                                                                                                                                                 |
@@ -145,15 +145,15 @@ widget:
 | preprocessor__categorical_pipeline__encoder__min_frequency                   |                                                                                                                                                 |
 | preprocessor__categorical_pipeline__encoder__sparse_output                   | False                                                                                                                                           |
 | preprocessor__feature_creation_pipeline__memory                              |                                                                                                                                                 |
-| preprocessor__feature_creation_pipeline__steps                               | [('feature_creation', FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br />              sparse_output=False))]                                                                                                                                                 |
+| preprocessor__feature_creation_pipeline__steps                               | [('feature_creation', FunctionTransformer(func=<function feature_creation at 0x0000025B889134C0>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br />              sparse_output=False))]                                                                                                                                                 |
 | preprocessor__feature_creation_pipeline__verbose                             | False                                                                                                                                           |
-| preprocessor__feature_creation_pipeline__feature_creation                    | FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)                                                                     |
+| preprocessor__feature_creation_pipeline__feature_creation                    | FunctionTransformer(func=<function feature_creation at 0x0000025B889134C0>)                                                                     |
 | preprocessor__feature_creation_pipeline__imputer                             | SimpleImputer(strategy='most_frequent')                                                                                                         |
 | preprocessor__feature_creation_pipeline__encoder                             | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br />              sparse_output=False)                                                                                                                                                 |
 | preprocessor__feature_creation_pipeline__feature_creation__accept_sparse     | False                                                                                                                                           |
 | preprocessor__feature_creation_pipeline__feature_creation__check_inverse     | True                                                                                                                                            |
 | preprocessor__feature_creation_pipeline__feature_creation__feature_names_out |                                                                                                                                                 |
-| preprocessor__feature_creation_pipeline__feature_creation__func              | <function feature_creation at 0x000001E7F14514E0>                                                                                               |
+| preprocessor__feature_creation_pipeline__feature_creation__func              | <function feature_creation at 0x0000025B889134C0>                                                                                               |
 | preprocessor__feature_creation_pipeline__feature_creation__inv_kw_args       |                                                                                                                                                 |
 | preprocessor__feature_creation_pipeline__feature_creation__inverse_func      |                                                                                                                                                 |
 | preprocessor__feature_creation_pipeline__feature_creation__kw_args           |                                                                                                                                                 |
@@ -173,7 +173,7 @@ widget:
 | preprocessor__feature_creation_pipeline__encoder__min_frequency              |                                                                                                                                                 |
 | preprocessor__feature_creation_pipeline__encoder__sparse_output              | False                                                                                                                                           |
 | feature-selection__k                                                         | all                                                                                                                                             |
-| feature-selection__score_func                                                | <function mutual_info_classif at 0x000001E7EDA4E480>                                                                                            |
+| feature-selection__score_func                                                | <function mutual_info_classif at 0x0000025B81CA7920>                                                                                            |
 | classifier__bootstrap                                                        | True                                                                                                                                            |
 | classifier__ccp_alpha                                                        | 0.0                                                                                                                                             |
 | classifier__class_weight                                                     |                                                                                                                                                 |
@@ -198,57 +198,57 @@ widget:
 
 ### Model Plot
 
-<style>#sk-container-id-22 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
-}#sk-container-id-22 {color: var(--sklearn-color-text);
-}#sk-container-id-22 pre {padding: 0;
-}#sk-container-id-22 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-22 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
-}#sk-container-id-22 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 thedefault 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-22 div.sk-text-repr-fallback {display: none;
+<style>#sk-container-id-7 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
+}#sk-container-id-7 {color: var(--sklearn-color-text);
+}#sk-container-id-7 pre {padding: 0;
+}#sk-container-id-7 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-7 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
+}#sk-container-id-7 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 thedefault 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-7 div.sk-text-repr-fallback {display: none;
 }div.sk-parallel-item,
 div.sk-serial,
 div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
-}/* Parallel-specific style estimator block */#sk-container-id-22 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
-}#sk-container-id-22 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
-}#sk-container-id-22 div.sk-parallel-item {display: flex;flex-direction: column;
-}#sk-container-id-22 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
-}#sk-container-id-22 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
-}#sk-container-id-22 div.sk-parallel-item:only-child::after {width: 0;
-}/* Serial-specific style estimator block */#sk-container-id-22 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
+}/* Parallel-specific style estimator block */#sk-container-id-7 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
+}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
+}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;
+}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
+}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
+}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;
+}/* Serial-specific style estimator block */#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
 }/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
 clickable and can be expanded/collapsed.
 - Pipeline and ColumnTransformer use this feature and define the default style
 - Estimators will overwrite some part of the style using the `sk-estimator` class
-*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-22 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
+*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-7 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
 }/* Toggleable label */
-#sk-container-id-22 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
-}#sk-container-id-22 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
-}#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
-}/* Toggleable content - dropdown */#sk-container-id-22 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-22 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
-}#sk-container-id-22 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-22 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
-}#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
-}#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
-}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-22 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
+#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
+}#sk-container-id-7 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
+}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
+}/* Toggleable content - dropdown */#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-7 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-7 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
+}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
+}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
 }/* Estimator-specific style *//* Colorize estimator box */
-#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-22 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
-}#sk-container-id-22 div.sk-label label.sk-toggleable__label,
-#sk-container-id-22 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
+#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+}#sk-container-id-7 div.sk-label label.sk-toggleable__label,
+#sk-container-id-7 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
 }/* On hover, darken the color of the background */
-#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
+#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
 }/* Label box, darken color on hover, fitted */
-#sk-container-id-22 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
-}/* Estimator label */#sk-container-id-22 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
-}#sk-container-id-22 div.sk-label-container {text-align: center;
+#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
+}/* Estimator label */#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
+}#sk-container-id-7 div.sk-label-container {text-align: center;
 }/* Estimator-specific */
-#sk-container-id-22 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-22 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+#sk-container-id-7 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-7 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
 }/* on hover */
-#sk-container-id-22 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-22 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+#sk-container-id-7 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-7 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
 }/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
 a:link.sk-estimator-doc-link,
 a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
@@ -268,13 +268,13 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
 .sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
 }.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
 }.sk-estimator-doc-link:hover span {display: block;
-}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-22 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-}#sk-container-id-22 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
+}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-7 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+}#sk-container-id-7 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
 }/* On hover */
-#sk-container-id-22 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
-}#sk-container-id-22 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
+#sk-container-id-7 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}#sk-container-id-7 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-22" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(transformers=[(&#x27;numerical_pipeline&#x27;,Pipeline(steps=[(&#x27;log_transformations&#x27;,FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,RobustScaler())]),[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;,&#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;, &#x27;age&#x27;]),(&#x27;categorical_pipeline&#x27;,Pipeline(steps=[(&#x27;as_categorical&#x27;,Funct...FunctionTransformer(func=&lt;function feature_creation at 0x000001E7F14514E0&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;most_frequent&#x27;)),(&#x27;encoder&#x27;,OneHotEncoder(drop=&#x27;first&#x27;,handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;age&#x27;])])),(&#x27;feature-selection&#x27;,SelectKBest(k=&#x27;all&#x27;,score_func=&lt;function mutual_info_classif at 0x000001E7EDA4E480&gt;)),(&#x27;classifier&#x27;,RandomForestClassifier(n_jobs=-1, random_state=2024))])</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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-298" type="checkbox" ><label for="sk-estimator-id-298" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(transformers=[(&#x27;numerical_pipeline&#x27;,Pipeline(steps=[(&#x27;log_transformations&#x27;,FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,RobustScaler())]),[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;,&#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;, &#x27;age&#x27;]),(&#x27;categorical_pipeline&#x27;,Pipeline(steps=[(&#x27;as_categorical&#x27;,Funct...FunctionTransformer(func=&lt;function feature_creation at 0x000001E7F14514E0&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;most_frequent&#x27;)),(&#x27;encoder&#x27;,OneHotEncoder(drop=&#x27;first&#x27;,handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;age&#x27;])])),(&#x27;feature-selection&#x27;,SelectKBest(k=&#x27;all&#x27;,score_func=&lt;function mutual_info_classif at 0x000001E7EDA4E480&gt;)),(&#x27;classifier&#x27;,RandomForestClassifier(n_jobs=-1, random_state=2024))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-299" type="checkbox" ><label for="sk-estimator-id-299" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;preprocessor: ColumnTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;numerical_pipeline&#x27;,Pipeline(steps=[(&#x27;log_transformations&#x27;,FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;, RobustScaler())]),[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;, &#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;,&#x27;age&#x27;]),(&#x27;categorical_pipeline&#x27;,Pipeline(steps=[(&#x27;as_categorical&#x27;,FunctionTransformer(func=&lt;function as_...handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;insurance&#x27;]),(&#x27;feature_creation_pipeline&#x27;,Pipeline(steps=[(&#x27;feature_creation&#x27;,FunctionTransformer(func=&lt;function feature_creation at 0x000001E7F14514E0&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;most_frequent&#x27;)),(&#x27;encoder&#x27;,OneHotEncoder(drop=&#x27;first&#x27;,handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;age&#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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-300" type="checkbox" ><label for="sk-estimator-id-300" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">numerical_pipeline</label><div class="sk-toggleable__content fitted"><pre>[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;, &#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;, &#x27;age&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-301" type="checkbox" ><label for="sk-estimator-id-301" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-302" type="checkbox" ><label for="sk-estimator-id-302" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-303" type="checkbox" ><label for="sk-estimator-id-303" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;RobustScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.RobustScaler.html">?<span>Documentation for RobustScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>RobustScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-304" type="checkbox" ><label for="sk-estimator-id-304" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">categorical_pipeline</label><div class="sk-toggleable__content fitted"><pre>[&#x27;insurance&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-305" type="checkbox" ><label for="sk-estimator-id-305" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function as_category at 0x000001E7F1450680&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-306" type="checkbox" ><label for="sk-estimator-id-306" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-307" type="checkbox" ><label for="sk-estimator-id-307" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-308" type="checkbox" ><label for="sk-estimator-id-308" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">feature_creation_pipeline</label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-309" type="checkbox" ><label for="sk-estimator-id-309" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function feature_creation at 0x000001E7F14514E0&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-310" type="checkbox" ><label for="sk-estimator-id-310" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-311" type="checkbox" ><label for="sk-estimator-id-311" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-312" type="checkbox" ><label for="sk-estimator-id-312" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SelectKBest<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.SelectKBest.html">?<span>Documentation for SelectKBest</span></a></label><div class="sk-toggleable__content fitted"><pre>SelectKBest(k=&#x27;all&#x27;,score_func=&lt;function mutual_info_classif at 0x000001E7EDA4E480&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-313" type="checkbox" ><label for="sk-estimator-id-313" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;RandomForestClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(n_jobs=-1, random_state=2024)</pre></div> </div></div></div></div></div></div>
+</style><div id="sk-container-id-7" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(transformers=[(&#x27;numerical_pipeline&#x27;,Pipeline(steps=[(&#x27;log_transformations&#x27;,FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,RobustScaler())]),[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;,&#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;, &#x27;age&#x27;]),(&#x27;categorical_pipeline&#x27;,Pipeline(steps=[(&#x27;as_categorical&#x27;,Funct...FunctionTransformer(func=&lt;function feature_creation at 0x0000025B889134C0&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;most_frequent&#x27;)),(&#x27;encoder&#x27;,OneHotEncoder(drop=&#x27;first&#x27;,handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;age&#x27;])])),(&#x27;feature-selection&#x27;,SelectKBest(k=&#x27;all&#x27;,score_func=&lt;function mutual_info_classif at 0x0000025B81CA7920&gt;)),(&#x27;classifier&#x27;,RandomForestClassifier(n_jobs=-1, random_state=2024))])</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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-58" type="checkbox" ><label for="sk-estimator-id-58" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(transformers=[(&#x27;numerical_pipeline&#x27;,Pipeline(steps=[(&#x27;log_transformations&#x27;,FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,RobustScaler())]),[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;,&#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;, &#x27;age&#x27;]),(&#x27;categorical_pipeline&#x27;,Pipeline(steps=[(&#x27;as_categorical&#x27;,Funct...FunctionTransformer(func=&lt;function feature_creation at 0x0000025B889134C0&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;most_frequent&#x27;)),(&#x27;encoder&#x27;,OneHotEncoder(drop=&#x27;first&#x27;,handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;age&#x27;])])),(&#x27;feature-selection&#x27;,SelectKBest(k=&#x27;all&#x27;,score_func=&lt;function mutual_info_classif at 0x0000025B81CA7920&gt;)),(&#x27;classifier&#x27;,RandomForestClassifier(n_jobs=-1, random_state=2024))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-59" type="checkbox" ><label for="sk-estimator-id-59" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;preprocessor: ColumnTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[(&#x27;numerical_pipeline&#x27;,Pipeline(steps=[(&#x27;log_transformations&#x27;,FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;, RobustScaler())]),[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;, &#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;,&#x27;age&#x27;]),(&#x27;categorical_pipeline&#x27;,Pipeline(steps=[(&#x27;as_categorical&#x27;,FunctionTransformer(func=&lt;function as_...handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;insurance&#x27;]),(&#x27;feature_creation_pipeline&#x27;,Pipeline(steps=[(&#x27;feature_creation&#x27;,FunctionTransformer(func=&lt;function feature_creation at 0x0000025B889134C0&gt;)),(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;most_frequent&#x27;)),(&#x27;encoder&#x27;,OneHotEncoder(drop=&#x27;first&#x27;,handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False))]),[&#x27;age&#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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-60" type="checkbox" ><label for="sk-estimator-id-60" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">numerical_pipeline</label><div class="sk-toggleable__content fitted"><pre>[&#x27;prg&#x27;, &#x27;pl&#x27;, &#x27;pr&#x27;, &#x27;sk&#x27;, &#x27;ts&#x27;, &#x27;m11&#x27;, &#x27;bd2&#x27;, &#x27;age&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-61" type="checkbox" ><label for="sk-estimator-id-61" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-62" type="checkbox" ><label for="sk-estimator-id-62" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-63" type="checkbox" ><label for="sk-estimator-id-63" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;RobustScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.RobustScaler.html">?<span>Documentation for RobustScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>RobustScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-64" type="checkbox" ><label for="sk-estimator-id-64" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">categorical_pipeline</label><div class="sk-toggleable__content fitted"><pre>[&#x27;insurance&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function as_category at 0x0000025B88910220&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted 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 fitted sk-toggleable__label-arrow fitted">feature_creation_pipeline</label><div class="sk-toggleable__content fitted"><pre>[&#x27;age&#x27;]</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;FunctionTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.FunctionTransformer.html">?<span>Documentation for FunctionTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>FunctionTransformer(func=&lt;function feature_creation at 0x0000025B889134C0&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;OneHotEncoder<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;infrequent_if_exist&#x27;,sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;SelectKBest<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.SelectKBest.html">?<span>Documentation for SelectKBest</span></a></label><div class="sk-toggleable__content fitted"><pre>SelectKBest(k=&#x27;all&#x27;,score_func=&lt;function mutual_info_classif at 0x0000025B81CA7920&gt;)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted">&nbsp;RandomForestClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(n_jobs=-1, random_state=2024)</pre></div> </div></div></div></div></div></div>
 
 ## Evaluation Results
 

RandomForestClassifier.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:74f7caaed8b8e54a554d5f73f8a0687bde553512541f78fd45cec04b5602e22b
+oid sha256:d0eb001bdaad0f4c68b7f9896903ae917d94fb73d89d642eab46ad30b0e7353c
 size 1320184