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XGBClassifier-Sepsis

Tabular Classification
Scikit-learn
Joblib
skops
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XGBClassifier-Sepsis / README.md
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metadata
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=<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, ...))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

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

model_description

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

roc_auc_curve

roc_auc_curve

feature_importances

feature_importances