---
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=
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=
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=
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=
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=
sparse_output=False))] |
| preprocessor__categorical_pipeline__verbose | False |
| preprocessor__categorical_pipeline__as_categorical | FunctionTransformer(func=
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 |
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.
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, ...))])
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'])])
['prg', 'pl', 'pr', 'sk', 'ts', 'm11', 'bd2', 'age']
FunctionTransformer(func=<ufunc 'log1p'>)
SimpleImputer(strategy='median')
RobustScaler()
['insurance']
FunctionTransformer(func=<function as_category at 0x0000013CE41B7600>)
SimpleImputer(strategy='most_frequent')
OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',sparse_output=False)
['age']
FunctionTransformer(func=<function feature_creation at 0x0000013CE41B7C40>)
SimpleImputer(strategy='most_frequent')
OneHotEncoder(drop='first', handle_unknown='ignore', sparse_output=False)
SelectKBest(k='all',score_func=<function mutual_info_classif at 0x0000013CE4234F40>)
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, ...)