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---
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library_name: sklearn
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license: mit
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tags:
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- sklearn
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- skops
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- tabular-classification
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model_format: pickle
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model_file: RandomForestClassifier.joblib
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widget:
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- structuredData:
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age:
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- 50
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- 31
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- 32
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bd2:
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- 0.627
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- 0.351
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- 0.672
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id:
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- ICU200010
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- ICU200011
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- ICU200012
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insurance:
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- 0
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- 0
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- 1
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m11:
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- 33.6
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- 26.6
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- 23.3
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pl:
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- 148
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- 85
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- 183
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pr:
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- 72
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- 66
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- 64
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prg:
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- 6
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- 1
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- 8
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sepsis:
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- Positive
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- Negative
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- Positive
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sk:
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- 35
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- 29
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- 0
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ts:
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- 0
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- 0
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- 0
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---
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# Model description
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[More Information Needed]
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## Intended uses & limitations
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[More Information Needed]
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## Training Procedure
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[More Information Needed]
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### Hyperparameters
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<details>
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<summary> Click to expand </summary>
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| Hyperparameter | Value |
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|------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| memory | |
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| 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 0x0000013CE41B7C40>)),<br /> ('imputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='ignore',<br /> sparse_output=False))]),<br /> ['age'])])), ('feature-selection', SelectKBest(k='all',<br /> score_func=<function mutual_info_classif at 0x0000013CE4234F40>)), ('classifier', RandomForestClassifier(n_jobs=-1, random_state=2024))] |
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| verbose | False |
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| 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 0x0000013CE41B7C40>)),<br /> ('imputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='ignore',<br /> sparse_output=False))]),<br /> ['age'])]) |
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| feature-selection | SelectKBest(k='all',<br /> score_func=<function mutual_info_classif at 0x0000013CE4234F40>) |
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| classifier | RandomForestClassifier(n_jobs=-1, random_state=2024) |
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| preprocessor__force_int_remainder_cols | True |
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| preprocessor__n_jobs | |
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| preprocessor__remainder | drop |
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| preprocessor__sparse_threshold | 0.3 |
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| preprocessor__transformer_weights | |
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| 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 0x0000013CE41B7600>)),<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 0x0000013CE41B7C40>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first', handle_unknown='ignore',<br /> sparse_output=False))]), ['age'])] |
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| preprocessor__verbose | False |
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| preprocessor__verbose_feature_names_out | True |
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| preprocessor__numerical_pipeline | Pipeline(steps=[('log_transformations',<br /> FunctionTransformer(func=<ufunc 'log1p'>)),<br /> ('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', RobustScaler())]) |
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| preprocessor__categorical_pipeline | Pipeline(steps=[('as_categorical',<br /> FunctionTransformer(func=<function as_category at 0x0000013CE41B7600>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]) |
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| preprocessor__feature_creation_pipeline | Pipeline(steps=[('feature_creation',<br /> FunctionTransformer(func=<function feature_creation at 0x0000013CE41B7C40>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first', handle_unknown='ignore',<br /> sparse_output=False))]) |
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| preprocessor__numerical_pipeline__memory | |
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| preprocessor__numerical_pipeline__steps | [('log_transformations', FunctionTransformer(func=<ufunc 'log1p'>)), ('imputer', SimpleImputer(strategy='median')), ('scaler', RobustScaler())] |
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| preprocessor__numerical_pipeline__verbose | False |
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| preprocessor__numerical_pipeline__log_transformations | FunctionTransformer(func=<ufunc 'log1p'>) |
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| preprocessor__numerical_pipeline__imputer | SimpleImputer(strategy='median') |
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| preprocessor__numerical_pipeline__scaler | RobustScaler() |
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| preprocessor__numerical_pipeline__log_transformations__accept_sparse | False |
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| preprocessor__numerical_pipeline__log_transformations__check_inverse | True |
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| preprocessor__numerical_pipeline__log_transformations__feature_names_out | |
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| preprocessor__numerical_pipeline__log_transformations__func | <ufunc 'log1p'> |
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| preprocessor__numerical_pipeline__log_transformations__inv_kw_args | |
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| preprocessor__numerical_pipeline__log_transformations__inverse_func | |
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| preprocessor__numerical_pipeline__log_transformations__kw_args | |
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| preprocessor__numerical_pipeline__log_transformations__validate | False |
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| preprocessor__numerical_pipeline__imputer__add_indicator | False |
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| preprocessor__numerical_pipeline__imputer__copy | True |
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| preprocessor__numerical_pipeline__imputer__fill_value | |
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| preprocessor__numerical_pipeline__imputer__keep_empty_features | False |
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| preprocessor__numerical_pipeline__imputer__missing_values | nan |
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| preprocessor__numerical_pipeline__imputer__strategy | median |
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| preprocessor__numerical_pipeline__scaler__copy | True |
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| preprocessor__numerical_pipeline__scaler__quantile_range | (25.0, 75.0) |
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| preprocessor__numerical_pipeline__scaler__unit_variance | False |
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| preprocessor__numerical_pipeline__scaler__with_centering | True |
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| preprocessor__numerical_pipeline__scaler__with_scaling | True |
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| preprocessor__categorical_pipeline__memory | |
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| preprocessor__categorical_pipeline__steps | [('as_categorical', FunctionTransformer(func=<function as_category at 0x0000013CE41B7600>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False))] |
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| preprocessor__categorical_pipeline__verbose | False |
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| preprocessor__categorical_pipeline__as_categorical | FunctionTransformer(func=<function as_category at 0x0000013CE41B7600>) |
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| preprocessor__categorical_pipeline__imputer | SimpleImputer(strategy='most_frequent') |
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| preprocessor__categorical_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False) |
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| preprocessor__categorical_pipeline__as_categorical__accept_sparse | False |
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| preprocessor__categorical_pipeline__as_categorical__check_inverse | True |
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| preprocessor__categorical_pipeline__as_categorical__feature_names_out | |
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| preprocessor__categorical_pipeline__as_categorical__func | <function as_category at 0x0000013CE41B7600> |
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| preprocessor__categorical_pipeline__as_categorical__inv_kw_args | |
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| preprocessor__categorical_pipeline__as_categorical__inverse_func | |
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| preprocessor__categorical_pipeline__as_categorical__kw_args | |
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| preprocessor__categorical_pipeline__as_categorical__validate | False |
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| preprocessor__categorical_pipeline__imputer__add_indicator | False |
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| preprocessor__categorical_pipeline__imputer__copy | True |
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| preprocessor__categorical_pipeline__imputer__fill_value | |
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| preprocessor__categorical_pipeline__imputer__keep_empty_features | False |
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| preprocessor__categorical_pipeline__imputer__missing_values | nan |
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| preprocessor__categorical_pipeline__imputer__strategy | most_frequent |
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| preprocessor__categorical_pipeline__encoder__categories | auto |
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| preprocessor__categorical_pipeline__encoder__drop | first |
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| preprocessor__categorical_pipeline__encoder__dtype | <class 'numpy.float64'> |
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| preprocessor__categorical_pipeline__encoder__feature_name_combiner | concat |
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| preprocessor__categorical_pipeline__encoder__handle_unknown | infrequent_if_exist |
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| preprocessor__categorical_pipeline__encoder__max_categories | |
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| preprocessor__categorical_pipeline__encoder__min_frequency | |
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| preprocessor__categorical_pipeline__encoder__sparse_output | False |
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| preprocessor__feature_creation_pipeline__memory | |
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| preprocessor__feature_creation_pipeline__steps | [('feature_creation', FunctionTransformer(func=<function feature_creation at 0x0000013CE41B7C40>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='ignore', sparse_output=False))] |
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| preprocessor__feature_creation_pipeline__verbose | False |
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| preprocessor__feature_creation_pipeline__feature_creation | FunctionTransformer(func=<function feature_creation at 0x0000013CE41B7C40>) |
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| preprocessor__feature_creation_pipeline__imputer | SimpleImputer(strategy='most_frequent') |
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| preprocessor__feature_creation_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='ignore', sparse_output=False) |
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| preprocessor__feature_creation_pipeline__feature_creation__accept_sparse | False |
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| preprocessor__feature_creation_pipeline__feature_creation__check_inverse | True |
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| preprocessor__feature_creation_pipeline__feature_creation__feature_names_out | |
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| preprocessor__feature_creation_pipeline__feature_creation__func | <function feature_creation at 0x0000013CE41B7C40> |
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| preprocessor__feature_creation_pipeline__feature_creation__inv_kw_args | |
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| preprocessor__feature_creation_pipeline__feature_creation__inverse_func | |
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| preprocessor__feature_creation_pipeline__feature_creation__kw_args | |
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| preprocessor__feature_creation_pipeline__feature_creation__validate | False |
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| preprocessor__feature_creation_pipeline__imputer__add_indicator | False |
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| preprocessor__feature_creation_pipeline__imputer__copy | True |
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| preprocessor__feature_creation_pipeline__imputer__fill_value | |
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| preprocessor__feature_creation_pipeline__imputer__keep_empty_features | False |
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| preprocessor__feature_creation_pipeline__imputer__missing_values | nan |
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| preprocessor__feature_creation_pipeline__imputer__strategy | most_frequent |
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| preprocessor__feature_creation_pipeline__encoder__categories | auto |
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| preprocessor__feature_creation_pipeline__encoder__drop | first |
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| preprocessor__feature_creation_pipeline__encoder__dtype | <class 'numpy.float64'> |
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| preprocessor__feature_creation_pipeline__encoder__feature_name_combiner | concat |
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| preprocessor__feature_creation_pipeline__encoder__handle_unknown | ignore |
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| preprocessor__feature_creation_pipeline__encoder__max_categories | |
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| preprocessor__feature_creation_pipeline__encoder__min_frequency | |
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| preprocessor__feature_creation_pipeline__encoder__sparse_output | False |
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| feature-selection__k | all |
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| feature-selection__score_func | <function mutual_info_classif at 0x0000013CE4234F40> |
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| classifier__bootstrap | True |
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| classifier__ccp_alpha | 0.0 |
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| classifier__class_weight | |
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| classifier__criterion | gini |
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| classifier__max_depth | |
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| classifier__max_features | sqrt |
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| classifier__max_leaf_nodes | |
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| classifier__max_samples | |
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| classifier__min_impurity_decrease | 0.0 |
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| classifier__min_samples_leaf | 1 |
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| classifier__min_samples_split | 2 |
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| classifier__min_weight_fraction_leaf | 0.0 |
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| classifier__monotonic_cst | |
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| classifier__n_estimators | 100 |
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| classifier__n_jobs | -1 |
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| classifier__oob_score | False |
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| classifier__random_state | 2024 |
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| classifier__verbose | 0 |
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| classifier__warm_start | False |
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</details>
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### Model Plot
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<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;}
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}#sk-container-id-7 {color: var(--sklearn-color-text);
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}#sk-container-id-7 pre {padding: 0;
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}#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;
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}#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);
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}#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;
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}#sk-container-id-7 div.sk-text-repr-fallback {display: none;
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}div.sk-parallel-item,
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div.sk-serial,
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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;
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}/* 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;
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}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
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}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;
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}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
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}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
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}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;
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}/* 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;
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}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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clickable and can be expanded/collapsed.
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- Pipeline and ColumnTransformer use this feature and define the default style
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- Estimators will overwrite some part of the style using the `sk-estimator` class
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*//* 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);
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}/* Toggleable label */
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#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;
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}#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);
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}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
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}/* 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);
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}#sk-container-id-7 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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}#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);
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}#sk-container-id-7 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
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}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
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}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
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}/* 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);
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}#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);
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}/* Estimator-specific style *//* Colorize estimator box */
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#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);
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}#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);
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}#sk-container-id-7 div.sk-label label.sk-toggleable__label,
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#sk-container-id-7 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
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}/* On hover, darken the color of the background */
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#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);
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}/* Label box, darken color on hover, fitted */
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#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);
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}/* Estimator label */#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
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}#sk-container-id-7 div.sk-label-container {text-align: center;
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}/* Estimator-specific */
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#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);
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}#sk-container-id-7 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
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}/* on hover */
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#sk-container-id-7 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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}#sk-container-id-7 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
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}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
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a:link.sk-estimator-doc-link,
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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);
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}.sk-estimator-doc-link.fitted,
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a:link.sk-estimator-doc-link.fitted,
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a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
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}/* On hover */
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div.sk-estimator:hover .sk-estimator-doc-link:hover,
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.sk-estimator-doc-link:hover,
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div.sk-label-container:hover .sk-estimator-doc-link:hover,
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.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
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.sk-estimator-doc-link.fitted:hover,
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div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
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.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
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}/* Span, style for the box shown on hovering the info icon */
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.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);
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}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
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}.sk-estimator-doc-link:hover span {display: block;
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}/* "?"-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;
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}#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);
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}/* On hover */
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#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;
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}#sk-container-id-7 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
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}
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</style><div id="sk-container-id-7" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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))])</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"> 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=[('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))])</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"> 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=[('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',FunctionTransformer(func=<function as_...handle_unknown='infrequent_if_exist',sparse_output=False))]),['insurance']),('feature_creation_pipeline',Pipeline(steps=[('feature_creation',FunctionTransformer(func=<function feature_creation at 0x0000013CE41B7C40>)),('imputer',SimpleImputer(strategy='most_frequent')),('encoder',OneHotEncoder(drop='first',handle_unknown='ignore',sparse_output=False))]),['age'])])</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>['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']</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"> 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=<ufunc 'log1p'>)</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"> 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='median')</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"> 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>['insurance']</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"> 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=<function as_category at 0x0000013CE41B7600>)</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"> 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='most_frequent')</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"> 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='first', handle_unknown='infrequent_if_exist',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>['age']</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"> 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=<function feature_creation at 0x0000013CE41B7C40>)</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"> 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='most_frequent')</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"> 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='first', handle_unknown='ignore', 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"> 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='all',score_func=<function mutual_info_classif at 0x0000013CE4234F40>)</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"> 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>
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## Evaluation Results
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[More Information Needed]
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# How to Get Started with the Model
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[More Information Needed]
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# Model Card Authors
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This model card is written by following authors:
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[More Information Needed]
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# Model Card Contact
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You can contact the model card authors through following channels:
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[More Information Needed]
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# Citation
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Below you can find information related to citation.
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**BibTeX:**
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```
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[More Information Needed]
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```
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# citation_bibtex
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bibtex
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@inproceedings{...,year={2024}}
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# get_started_code
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import joblib
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clf = joblib.load(../models/RandomForestClassifier.joblib)
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# model_card_authors
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Gabriel Okundaye
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# limitations
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This model needs further feature engineering to improve the f1 weighted score. Collaborate on with me here [GitHub](https://github.com/D0nG4667/sepsis_prediction_full_stack)
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# model_description
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This is a RandomForestClassifier model trained on Sepsis dataset from this [kaggle dataset](https://www.kaggle.com/datasets/chaunguynnghunh/sepsis/data).
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# roc_auc_curve
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.webp)
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# feature_importances
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.webp)
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