diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -72,127 +72,127 @@ widget:
Click to expand -| Hyperparameter | Value | -|------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------| -| memory | | -| steps | [('preprocessor', ColumnTransformer(transformers=[('numerical_pipeline',
Pipeline(steps=[('log_transformations',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='median')),
('scaler', RobustScaler())]),
['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',
'age']),
('categorical_pipeline',
Pipeline(steps=[('as_categorical',
FunctionTransformer(func= handle_unknown='infrequent_if_exist',
sparse_output=False))]),
['insurance']),
('feature_creation_pipeline',
Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]),
['age'])])), ('feature-selection', SelectKBest(k='all',
score_func=)), ('classifier', RandomForestClassifier(n_jobs=-1, random_state=2024))] | -| verbose | False | -| preprocessor | ColumnTransformer(transformers=[('numerical_pipeline',
Pipeline(steps=[('log_transformations',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='median')),
('scaler', RobustScaler())]),
['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',
'age']),
('categorical_pipeline',
Pipeline(steps=[('as_categorical',
FunctionTransformer(func= handle_unknown='infrequent_if_exist',
sparse_output=False))]),
['insurance']),
('feature_creation_pipeline',
Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]),
['age'])]) | -| feature-selection | SelectKBest(k='all',
score_func=) | -| 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',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='median')),
('scaler', RobustScaler())]), ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']), ('categorical_pipeline', Pipeline(steps=[('as_categorical',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]), ['insurance']), ('feature_creation_pipeline', Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]), ['age'])] | -| preprocessor__verbose | False | -| preprocessor__verbose_feature_names_out | True | -| preprocessor__numerical_pipeline | Pipeline(steps=[('log_transformations',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='median')),
('scaler', RobustScaler())]) | -| preprocessor__categorical_pipeline | Pipeline(steps=[('as_categorical',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]) | -| preprocessor__feature_creation_pipeline | Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]) | -| preprocessor__numerical_pipeline__memory | | -| preprocessor__numerical_pipeline__steps | [('log_transformations', FunctionTransformer(func=)), ('imputer', SimpleImputer(strategy='median')), ('scaler', RobustScaler())] | -| preprocessor__numerical_pipeline__verbose | False | -| preprocessor__numerical_pipeline__log_transformations | FunctionTransformer(func=) | -| preprocessor__numerical_pipeline__imputer | SimpleImputer(strategy='median') | -| preprocessor__numerical_pipeline__scaler | RobustScaler() | -| preprocessor__numerical_pipeline__log_transformations__accept_sparse | False | -| preprocessor__numerical_pipeline__log_transformations__check_inverse | True | -| preprocessor__numerical_pipeline__log_transformations__feature_names_out | | -| preprocessor__numerical_pipeline__log_transformations__func | | -| preprocessor__numerical_pipeline__log_transformations__inv_kw_args | | -| preprocessor__numerical_pipeline__log_transformations__inverse_func | | -| preprocessor__numerical_pipeline__log_transformations__kw_args | | -| preprocessor__numerical_pipeline__log_transformations__validate | False | -| preprocessor__numerical_pipeline__imputer__add_indicator | False | -| preprocessor__numerical_pipeline__imputer__copy | True | -| preprocessor__numerical_pipeline__imputer__fill_value | | -| preprocessor__numerical_pipeline__imputer__keep_empty_features | False | -| preprocessor__numerical_pipeline__imputer__missing_values | nan | -| preprocessor__numerical_pipeline__imputer__strategy | median | -| preprocessor__numerical_pipeline__scaler__copy | True | -| preprocessor__numerical_pipeline__scaler__quantile_range | (25.0, 75.0) | -| preprocessor__numerical_pipeline__scaler__unit_variance | False | -| 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=)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',
sparse_output=False))] | -| preprocessor__categorical_pipeline__verbose | False | -| preprocessor__categorical_pipeline__as_categorical | FunctionTransformer(func=) | -| preprocessor__categorical_pipeline__imputer | SimpleImputer(strategy='most_frequent') | -| preprocessor__categorical_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',
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 | | -| preprocessor__categorical_pipeline__as_categorical__inv_kw_args | | -| preprocessor__categorical_pipeline__as_categorical__inverse_func | | -| preprocessor__categorical_pipeline__as_categorical__kw_args | | -| preprocessor__categorical_pipeline__as_categorical__validate | False | -| preprocessor__categorical_pipeline__imputer__add_indicator | False | -| preprocessor__categorical_pipeline__imputer__copy | True | -| preprocessor__categorical_pipeline__imputer__fill_value | | -| preprocessor__categorical_pipeline__imputer__keep_empty_features | False | -| preprocessor__categorical_pipeline__imputer__missing_values | nan | -| preprocessor__categorical_pipeline__imputer__strategy | most_frequent | -| preprocessor__categorical_pipeline__encoder__categories | auto | -| preprocessor__categorical_pipeline__encoder__drop | first | -| preprocessor__categorical_pipeline__encoder__dtype | | -| preprocessor__categorical_pipeline__encoder__feature_name_combiner | concat | -| preprocessor__categorical_pipeline__encoder__handle_unknown | infrequent_if_exist | -| preprocessor__categorical_pipeline__encoder__max_categories | | -| 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=)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',
sparse_output=False))] | -| preprocessor__feature_creation_pipeline__verbose | False | -| preprocessor__feature_creation_pipeline__feature_creation | FunctionTransformer(func=) | -| preprocessor__feature_creation_pipeline__imputer | SimpleImputer(strategy='most_frequent') | -| preprocessor__feature_creation_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',
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 | | -| 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 | | -| preprocessor__feature_creation_pipeline__feature_creation__validate | False | -| preprocessor__feature_creation_pipeline__imputer__add_indicator | False | -| preprocessor__feature_creation_pipeline__imputer__copy | True | -| preprocessor__feature_creation_pipeline__imputer__fill_value | | -| preprocessor__feature_creation_pipeline__imputer__keep_empty_features | False | -| preprocessor__feature_creation_pipeline__imputer__missing_values | nan | -| preprocessor__feature_creation_pipeline__imputer__strategy | most_frequent | -| preprocessor__feature_creation_pipeline__encoder__categories | auto | -| preprocessor__feature_creation_pipeline__encoder__drop | first | -| preprocessor__feature_creation_pipeline__encoder__dtype | | -| preprocessor__feature_creation_pipeline__encoder__feature_name_combiner | concat | -| preprocessor__feature_creation_pipeline__encoder__handle_unknown | infrequent_if_exist | -| preprocessor__feature_creation_pipeline__encoder__max_categories | | -| preprocessor__feature_creation_pipeline__encoder__min_frequency | | -| preprocessor__feature_creation_pipeline__encoder__sparse_output | False | -| feature-selection__k | all | -| feature-selection__score_func | | -| classifier__bootstrap | True | -| classifier__ccp_alpha | 0.0 | -| classifier__class_weight | | -| classifier__criterion | gini | -| classifier__max_depth | | -| classifier__max_features | sqrt | -| classifier__max_leaf_nodes | | -| classifier__max_samples | | -| classifier__min_impurity_decrease | 0.0 | -| classifier__min_samples_leaf | 1 | -| classifier__min_samples_split | 2 | -| classifier__min_weight_fraction_leaf | 0.0 | -| classifier__monotonic_cst | | -| classifier__n_estimators | 100 | -| classifier__n_jobs | -1 | -| classifier__oob_score | False | -| classifier__random_state | 2024 | -| classifier__verbose | 0 | -| classifier__warm_start | False | +| Hyperparameter | Value | +|------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| memory | | +| steps | [('preprocessor', ColumnTransformer(transformers=[('numerical_pipeline',
Pipeline(steps=[('log_transformations',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='median')),
('scaler', RobustScaler())]),
['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',
'age']),
('categorical_pipeline',
Pipeline(steps=[('as_categorical',
FunctionTransformer(func= handle_unknown='infrequent_if_exist',
sparse_output=False))]),
['insurance']),
('feature_creation_pipeline',
Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='ignore',
sparse_output=False))]),
['age'])])), ('feature-selection', SelectKBest(k='all',
score_func=)), ('classifier', RandomForestClassifier(n_jobs=-1, random_state=2024))] | +| verbose | False | +| preprocessor | ColumnTransformer(transformers=[('numerical_pipeline',
Pipeline(steps=[('log_transformations',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='median')),
('scaler', RobustScaler())]),
['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2',
'age']),
('categorical_pipeline',
Pipeline(steps=[('as_categorical',
FunctionTransformer(func= handle_unknown='infrequent_if_exist',
sparse_output=False))]),
['insurance']),
('feature_creation_pipeline',
Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer',
SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='ignore',
sparse_output=False))]),
['age'])]) | +| feature-selection | SelectKBest(k='all',
score_func=) | +| 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',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='median')),
('scaler', RobustScaler())]), ['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']), ('categorical_pipeline', Pipeline(steps=[('as_categorical',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]), ['insurance']), ('feature_creation_pipeline', Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first', handle_unknown='ignore',
sparse_output=False))]), ['age'])] | +| preprocessor__verbose | False | +| preprocessor__verbose_feature_names_out | True | +| preprocessor__numerical_pipeline | Pipeline(steps=[('log_transformations',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='median')),
('scaler', RobustScaler())]) | +| preprocessor__categorical_pipeline | Pipeline(steps=[('as_categorical',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first',
handle_unknown='infrequent_if_exist',
sparse_output=False))]) | +| preprocessor__feature_creation_pipeline | Pipeline(steps=[('feature_creation',
FunctionTransformer(func=)),
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder',
OneHotEncoder(drop='first', handle_unknown='ignore',
sparse_output=False))]) | +| preprocessor__numerical_pipeline__memory | | +| preprocessor__numerical_pipeline__steps | [('log_transformations', FunctionTransformer(func=)), ('imputer', SimpleImputer(strategy='median')), ('scaler', RobustScaler())] | +| preprocessor__numerical_pipeline__verbose | False | +| preprocessor__numerical_pipeline__log_transformations | FunctionTransformer(func=) | +| preprocessor__numerical_pipeline__imputer | SimpleImputer(strategy='median') | +| preprocessor__numerical_pipeline__scaler | RobustScaler() | +| preprocessor__numerical_pipeline__log_transformations__accept_sparse | False | +| preprocessor__numerical_pipeline__log_transformations__check_inverse | True | +| preprocessor__numerical_pipeline__log_transformations__feature_names_out | | +| preprocessor__numerical_pipeline__log_transformations__func | | +| preprocessor__numerical_pipeline__log_transformations__inv_kw_args | | +| preprocessor__numerical_pipeline__log_transformations__inverse_func | | +| preprocessor__numerical_pipeline__log_transformations__kw_args | | +| preprocessor__numerical_pipeline__log_transformations__validate | False | +| preprocessor__numerical_pipeline__imputer__add_indicator | False | +| preprocessor__numerical_pipeline__imputer__copy | True | +| preprocessor__numerical_pipeline__imputer__fill_value | | +| preprocessor__numerical_pipeline__imputer__keep_empty_features | False | +| preprocessor__numerical_pipeline__imputer__missing_values | nan | +| preprocessor__numerical_pipeline__imputer__strategy | median | +| preprocessor__numerical_pipeline__scaler__copy | True | +| preprocessor__numerical_pipeline__scaler__quantile_range | (25.0, 75.0) | +| preprocessor__numerical_pipeline__scaler__unit_variance | False | +| 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=)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',
sparse_output=False))] | +| preprocessor__categorical_pipeline__verbose | False | +| preprocessor__categorical_pipeline__as_categorical | FunctionTransformer(func=) | +| preprocessor__categorical_pipeline__imputer | SimpleImputer(strategy='most_frequent') | +| preprocessor__categorical_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',
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 | | +| preprocessor__categorical_pipeline__as_categorical__inv_kw_args | | +| preprocessor__categorical_pipeline__as_categorical__inverse_func | | +| preprocessor__categorical_pipeline__as_categorical__kw_args | | +| preprocessor__categorical_pipeline__as_categorical__validate | False | +| preprocessor__categorical_pipeline__imputer__add_indicator | False | +| preprocessor__categorical_pipeline__imputer__copy | True | +| preprocessor__categorical_pipeline__imputer__fill_value | | +| preprocessor__categorical_pipeline__imputer__keep_empty_features | False | +| preprocessor__categorical_pipeline__imputer__missing_values | nan | +| preprocessor__categorical_pipeline__imputer__strategy | most_frequent | +| preprocessor__categorical_pipeline__encoder__categories | auto | +| preprocessor__categorical_pipeline__encoder__drop | first | +| preprocessor__categorical_pipeline__encoder__dtype | | +| preprocessor__categorical_pipeline__encoder__feature_name_combiner | concat | +| preprocessor__categorical_pipeline__encoder__handle_unknown | infrequent_if_exist | +| preprocessor__categorical_pipeline__encoder__max_categories | | +| 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=)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='ignore', sparse_output=False))] | +| preprocessor__feature_creation_pipeline__verbose | False | +| preprocessor__feature_creation_pipeline__feature_creation | FunctionTransformer(func=) | +| preprocessor__feature_creation_pipeline__imputer | SimpleImputer(strategy='most_frequent') | +| preprocessor__feature_creation_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='ignore', 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 | | +| 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 | | +| preprocessor__feature_creation_pipeline__feature_creation__validate | False | +| preprocessor__feature_creation_pipeline__imputer__add_indicator | False | +| preprocessor__feature_creation_pipeline__imputer__copy | True | +| preprocessor__feature_creation_pipeline__imputer__fill_value | | +| preprocessor__feature_creation_pipeline__imputer__keep_empty_features | False | +| preprocessor__feature_creation_pipeline__imputer__missing_values | nan | +| preprocessor__feature_creation_pipeline__imputer__strategy | most_frequent | +| preprocessor__feature_creation_pipeline__encoder__categories | auto | +| preprocessor__feature_creation_pipeline__encoder__drop | first | +| preprocessor__feature_creation_pipeline__encoder__dtype | | +| preprocessor__feature_creation_pipeline__encoder__feature_name_combiner | concat | +| preprocessor__feature_creation_pipeline__encoder__handle_unknown | ignore | +| preprocessor__feature_creation_pipeline__encoder__max_categories | | +| preprocessor__feature_creation_pipeline__encoder__min_frequency | | +| preprocessor__feature_creation_pipeline__encoder__sparse_output | False | +| feature-selection__k | all | +| feature-selection__score_func | | +| classifier__bootstrap | True | +| classifier__ccp_alpha | 0.0 | +| classifier__class_weight | | +| classifier__criterion | gini | +| classifier__max_depth | | +| classifier__max_features | sqrt | +| classifier__max_leaf_nodes | | +| classifier__max_samples | | +| classifier__min_impurity_decrease | 0.0 | +| classifier__min_samples_leaf | 1 | +| classifier__min_samples_split | 2 | +| classifier__min_weight_fraction_leaf | 0.0 | +| classifier__monotonic_cst | | +| classifier__n_estimators | 100 | +| classifier__n_jobs | -1 | +| classifier__oob_score | False | +| classifier__random_state | 2024 | +| classifier__verbose | 0 | +| classifier__warm_start | False |
@@ -274,7 +274,7 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, #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); } -
Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('numerical_pipeline',Pipeline(steps=[('log_transformations',FunctionTransformer(func=<ufunc 'log1p'>)),('imputer',SimpleImputer(strategy='median')),('scaler',RobustScaler())]),['prg', 'pl', 'pr', 'sk','ts', 'm11', 'bd2', 'age']),('categorical_pipeline',Pipeline(steps=[('as_categorical',Funct...FunctionTransformer(func=<function feature_creation at 0x00000147012327A0>)),('imputer',SimpleImputer(strategy='most_frequent')),('encoder',OneHotEncoder(drop='first',handle_unknown='infrequent_if_exist',sparse_output=False))]),['age'])])),('feature-selection',SelectKBest(k='all',score_func=<function mutual_info_classif at 0x000001470129BA60>)),('classifier',RandomForestClassifier(n_jobs=-1, random_state=2024))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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+
Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('numerical_pipeline',Pipeline(steps=[('log_transformations',FunctionTransformer(func=<ufunc 'log1p'>)),('imputer',SimpleImputer(strategy='median')),('scaler',RobustScaler())]),['prg', 'pl', 'pr', 'sk','ts', 'm11', 'bd2', 'age']),('categorical_pipeline',Pipeline(steps=[('as_categorical',Funct...FunctionTransformer(func=<function feature_creation at 0x0000013CE41B7C40>)),('imputer',SimpleImputer(strategy='most_frequent')),('encoder',OneHotEncoder(drop='first',handle_unknown='ignore',sparse_output=False))]),['age'])])),('feature-selection',SelectKBest(k='all',score_func=<function mutual_info_classif at 0x0000013CE4234F40>)),('classifier',RandomForestClassifier(n_jobs=-1, random_state=2024))])
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## Evaluation Results