--- library_name: sklearn license: mit tags: - sklearn - skops - tabular-classification model_format: pickle model_file: XGBClassifier.joblib widget: - structuredData: age: - 50 - 31 - 32 bd2: - 0.627 - 0.351 - 0.672 id: - ICU200010 - ICU200011 - ICU200012 insurance: - 0 - 0 - 1 m11: - 33.6 - 26.6 - 23.3 pl: - 148 - 85 - 183 pr: - 72 - 66 - 64 prg: - 6 - 1 - 8 sepsis: - Positive - Negative - Positive sk: - 35 - 29 - 0 ts: - 0 - 0 - 0 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
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='ignore',
sparse_output=False))]),
['age'])])), ('feature-selection', SelectKBest(k='all',
score_func=)), ('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=)),
('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 | 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=)),
('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__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 [More Information Needed] # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # 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](https://github.com/D0nG4667/sepsis_prediction_full_stack) # model_description This is a XGBClassifier model trained on Sepsis dataset from this [kaggle dataset](https://www.kaggle.com/datasets/chaunguynnghunh/sepsis/data). # roc_auc_curve ![roc_auc_curve](ROC_AUC_Curve_for_RandomForestClassifier_and_XGBClassifier_(F1-Weighted_Scores__0.778_and_0.777_respectively).webp) # feature_importances ![feature_importances](Feature_Importances-_XGBClassifier_(F1-Weighted_Scores__0.777).webp)