gabcares/XGBClassifier-Sepsis

Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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Hyperparameter	Value
memory
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', 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'])])), ('feature-selection', SelectKBest(k='all', score_func=<function mutual_info_classif at 0x0000013CE4234F40>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, device=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=20, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, multi_strategy=None, n_estimators=10, n_jobs=-1, num_parallel_tree=None, random_state=2024, ...))]
verbose	False
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', 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'])])
feature-selection	SelectKBest(k='all', score_func=<function mutual_info_classif at 0x0000013CE4234F40>)
classifier	XGBClassifier(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, device=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=20, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, multi_strategy=None, n_estimators=10, n_jobs=-1, num_parallel_tree=None, 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=<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_category at 0x0000013CE41B7600>)), ('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=<function feature_creation at 0x0000013CE41B7C40>)), ('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=<ufunc 'log1p'>)), ('imputer', SimpleImputer(strategy='median')), ('scaler', RobustScaler())])
preprocessor__categorical_pipeline	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', sparse_output=False))])
preprocessor__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))])
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
preprocessor__numerical_pipeline__log_transformations	FunctionTransformer(func=<ufunc 'log1p'>)
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	<ufunc 'log1p'>
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=<function as_category at 0x0000013CE41B7600>)), ('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=<function as_category at 0x0000013CE41B7600>)
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	<function as_category at 0x0000013CE41B7600>
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	<class 'numpy.float64'>
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=<function feature_creation at 0x0000013CE41B7C40>)), ('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=<function feature_creation at 0x0000013CE41B7C40>)
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	<function feature_creation at 0x0000013CE41B7C40>
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	<class 'numpy.float64'>
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	<function mutual_info_classif at 0x0000013CE4234F40>
classifier__objective	binary:logistic
classifier__base_score
classifier__booster
classifier__callbacks
classifier__colsample_bylevel
classifier__colsample_bynode
classifier__colsample_bytree
classifier__device
classifier__early_stopping_rounds
classifier__enable_categorical	False
classifier__eval_metric
classifier__feature_types
classifier__gamma
classifier__grow_policy
classifier__importance_type
classifier__interaction_constraints
classifier__learning_rate
classifier__max_bin
classifier__max_cat_threshold
classifier__max_cat_to_onehot
classifier__max_delta_step
classifier__max_depth	20
classifier__max_leaves
classifier__min_child_weight
classifier__missing	nan
classifier__monotone_constraints
classifier__multi_strategy
classifier__n_estimators	10
classifier__n_jobs	-1
classifier__num_parallel_tree
classifier__random_state	2024
classifier__reg_alpha
classifier__reg_lambda
classifier__sampling_method
classifier__scale_pos_weight
classifier__subsample
classifier__tree_method
classifier__validate_parameters
classifier__verbosity
classifier__verbose	0

Model Plot

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...feature_types=None, gamma=None, grow_policy=None,importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=20, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=10, n_jobs=-1,num_parallel_tree=None, random_state=2024, ...))])

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Evaluation Results

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How to Get Started with the Model

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Model Card Authors

This model card is written by following authors:

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Model Card Contact

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Citation

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BibTeX:

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citation_bibtex

bibtex @inproceedings{...,year={2024}}

get_started_code

import joblib clf = joblib.load(../models/XGBClassifier.joblib)

model_card_authors

Gabriel Okundaye

limitations

This model needs further feature engineering to improve the f1 weighted score. Collaborate on with me here GitHub

model_description

This is a XGBClassifier model trained on Sepsis dataset from this kaggle dataset.

roc_auc_curve

feature_importances