Pushing files to the repo-gabcares/XGBClassifier-Sepsis from the directory- ../models/huggingface/XGBClassifier
Browse files- README.md +356 -0
- XGBClassifier.joblib +3 -0
- config.json +83 -0
README.md
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|
| 1 |
+
---
|
| 2 |
+
library_name: sklearn
|
| 3 |
+
license: mit
|
| 4 |
+
tags:
|
| 5 |
+
- sklearn
|
| 6 |
+
- skops
|
| 7 |
+
- tabular-classification
|
| 8 |
+
model_format: pickle
|
| 9 |
+
model_file: XGBClassifier.joblib
|
| 10 |
+
widget:
|
| 11 |
+
- structuredData:
|
| 12 |
+
age:
|
| 13 |
+
- 50
|
| 14 |
+
- 31
|
| 15 |
+
- 32
|
| 16 |
+
bd2:
|
| 17 |
+
- 0.627
|
| 18 |
+
- 0.351
|
| 19 |
+
- 0.672
|
| 20 |
+
id:
|
| 21 |
+
- ICU200010
|
| 22 |
+
- ICU200011
|
| 23 |
+
- ICU200012
|
| 24 |
+
insurance:
|
| 25 |
+
- 0
|
| 26 |
+
- 0
|
| 27 |
+
- 1
|
| 28 |
+
m11:
|
| 29 |
+
- 33.6
|
| 30 |
+
- 26.6
|
| 31 |
+
- 23.3
|
| 32 |
+
pl:
|
| 33 |
+
- 148
|
| 34 |
+
- 85
|
| 35 |
+
- 183
|
| 36 |
+
pr:
|
| 37 |
+
- 72
|
| 38 |
+
- 66
|
| 39 |
+
- 64
|
| 40 |
+
prg:
|
| 41 |
+
- 6
|
| 42 |
+
- 1
|
| 43 |
+
- 8
|
| 44 |
+
sepsis:
|
| 45 |
+
- Positive
|
| 46 |
+
- Negative
|
| 47 |
+
- Positive
|
| 48 |
+
sk:
|
| 49 |
+
- 35
|
| 50 |
+
- 29
|
| 51 |
+
- 0
|
| 52 |
+
ts:
|
| 53 |
+
- 0
|
| 54 |
+
- 0
|
| 55 |
+
- 0
|
| 56 |
+
---
|
| 57 |
+
|
| 58 |
+
# Model description
|
| 59 |
+
|
| 60 |
+
[More Information Needed]
|
| 61 |
+
|
| 62 |
+
## Intended uses & limitations
|
| 63 |
+
|
| 64 |
+
[More Information Needed]
|
| 65 |
+
|
| 66 |
+
## Training Procedure
|
| 67 |
+
|
| 68 |
+
[More Information Needed]
|
| 69 |
+
|
| 70 |
+
### Hyperparameters
|
| 71 |
+
|
| 72 |
+
<details>
|
| 73 |
+
<summary> Click to expand </summary>
|
| 74 |
+
|
| 75 |
+
| Hyperparameter | Value |
|
| 76 |
+
|------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 77 |
+
| memory | |
|
| 78 |
+
| 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 0x000001E7F14514E0>)),<br /> ('imputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]),<br /> ['age'])])), ('feature-selection', SelectKBest(k='all',<br /> score_func=<function mutual_info_classif at 0x000001E7EDA4E480>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=20, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=10, n_jobs=-1,<br /> num_parallel_tree=None, random_state=2024, ...))] |
|
| 79 |
+
| verbose | False |
|
| 80 |
+
| 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 0x000001E7F14514E0>)),<br /> ('imputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]),<br /> ['age'])]) |
|
| 81 |
+
| feature-selection | SelectKBest(k='all',<br /> score_func=<function mutual_info_classif at 0x000001E7EDA4E480>) |
|
| 82 |
+
| classifier | XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=20, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=10, n_jobs=-1,<br /> num_parallel_tree=None, random_state=2024, ...) |
|
| 83 |
+
| preprocessor__force_int_remainder_cols | True |
|
| 84 |
+
| preprocessor__n_jobs | |
|
| 85 |
+
| preprocessor__remainder | drop |
|
| 86 |
+
| preprocessor__sparse_threshold | 0.3 |
|
| 87 |
+
| preprocessor__transformer_weights | |
|
| 88 |
+
| 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 0x000001E7F1450680>)),<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 0x000001E7F14514E0>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]), ['age'])] |
|
| 89 |
+
| preprocessor__verbose | False |
|
| 90 |
+
| preprocessor__verbose_feature_names_out | True |
|
| 91 |
+
| preprocessor__numerical_pipeline | Pipeline(steps=[('log_transformations',<br /> FunctionTransformer(func=<ufunc 'log1p'>)),<br /> ('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', RobustScaler())]) |
|
| 92 |
+
| preprocessor__categorical_pipeline | Pipeline(steps=[('as_categorical',<br /> FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]) |
|
| 93 |
+
| preprocessor__feature_creation_pipeline | Pipeline(steps=[('feature_creation',<br /> FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)),<br /> ('imputer', SimpleImputer(strategy='most_frequent')),<br /> ('encoder',<br /> OneHotEncoder(drop='first',<br /> handle_unknown='infrequent_if_exist',<br /> sparse_output=False))]) |
|
| 94 |
+
| preprocessor__numerical_pipeline__memory | |
|
| 95 |
+
| preprocessor__numerical_pipeline__steps | [('log_transformations', FunctionTransformer(func=<ufunc 'log1p'>)), ('imputer', SimpleImputer(strategy='median')), ('scaler', RobustScaler())] |
|
| 96 |
+
| preprocessor__numerical_pipeline__verbose | False |
|
| 97 |
+
| preprocessor__numerical_pipeline__log_transformations | FunctionTransformer(func=<ufunc 'log1p'>) |
|
| 98 |
+
| preprocessor__numerical_pipeline__imputer | SimpleImputer(strategy='median') |
|
| 99 |
+
| preprocessor__numerical_pipeline__scaler | RobustScaler() |
|
| 100 |
+
| preprocessor__numerical_pipeline__log_transformations__accept_sparse | False |
|
| 101 |
+
| preprocessor__numerical_pipeline__log_transformations__check_inverse | True |
|
| 102 |
+
| preprocessor__numerical_pipeline__log_transformations__feature_names_out | |
|
| 103 |
+
| preprocessor__numerical_pipeline__log_transformations__func | <ufunc 'log1p'> |
|
| 104 |
+
| preprocessor__numerical_pipeline__log_transformations__inv_kw_args | |
|
| 105 |
+
| preprocessor__numerical_pipeline__log_transformations__inverse_func | |
|
| 106 |
+
| preprocessor__numerical_pipeline__log_transformations__kw_args | |
|
| 107 |
+
| preprocessor__numerical_pipeline__log_transformations__validate | False |
|
| 108 |
+
| preprocessor__numerical_pipeline__imputer__add_indicator | False |
|
| 109 |
+
| preprocessor__numerical_pipeline__imputer__copy | True |
|
| 110 |
+
| preprocessor__numerical_pipeline__imputer__fill_value | |
|
| 111 |
+
| preprocessor__numerical_pipeline__imputer__keep_empty_features | False |
|
| 112 |
+
| preprocessor__numerical_pipeline__imputer__missing_values | nan |
|
| 113 |
+
| preprocessor__numerical_pipeline__imputer__strategy | median |
|
| 114 |
+
| preprocessor__numerical_pipeline__scaler__copy | True |
|
| 115 |
+
| preprocessor__numerical_pipeline__scaler__quantile_range | (25.0, 75.0) |
|
| 116 |
+
| preprocessor__numerical_pipeline__scaler__unit_variance | False |
|
| 117 |
+
| preprocessor__numerical_pipeline__scaler__with_centering | True |
|
| 118 |
+
| preprocessor__numerical_pipeline__scaler__with_scaling | True |
|
| 119 |
+
| preprocessor__categorical_pipeline__memory | |
|
| 120 |
+
| preprocessor__categorical_pipeline__steps | [('as_categorical', FunctionTransformer(func=<function as_category at 0x000001E7F1450680>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False))] |
|
| 121 |
+
| preprocessor__categorical_pipeline__verbose | False |
|
| 122 |
+
| preprocessor__categorical_pipeline__as_categorical | FunctionTransformer(func=<function as_category at 0x000001E7F1450680>) |
|
| 123 |
+
| preprocessor__categorical_pipeline__imputer | SimpleImputer(strategy='most_frequent') |
|
| 124 |
+
| preprocessor__categorical_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False) |
|
| 125 |
+
| preprocessor__categorical_pipeline__as_categorical__accept_sparse | False |
|
| 126 |
+
| preprocessor__categorical_pipeline__as_categorical__check_inverse | True |
|
| 127 |
+
| preprocessor__categorical_pipeline__as_categorical__feature_names_out | |
|
| 128 |
+
| preprocessor__categorical_pipeline__as_categorical__func | <function as_category at 0x000001E7F1450680> |
|
| 129 |
+
| preprocessor__categorical_pipeline__as_categorical__inv_kw_args | |
|
| 130 |
+
| preprocessor__categorical_pipeline__as_categorical__inverse_func | |
|
| 131 |
+
| preprocessor__categorical_pipeline__as_categorical__kw_args | |
|
| 132 |
+
| preprocessor__categorical_pipeline__as_categorical__validate | False |
|
| 133 |
+
| preprocessor__categorical_pipeline__imputer__add_indicator | False |
|
| 134 |
+
| preprocessor__categorical_pipeline__imputer__copy | True |
|
| 135 |
+
| preprocessor__categorical_pipeline__imputer__fill_value | |
|
| 136 |
+
| preprocessor__categorical_pipeline__imputer__keep_empty_features | False |
|
| 137 |
+
| preprocessor__categorical_pipeline__imputer__missing_values | nan |
|
| 138 |
+
| preprocessor__categorical_pipeline__imputer__strategy | most_frequent |
|
| 139 |
+
| preprocessor__categorical_pipeline__encoder__categories | auto |
|
| 140 |
+
| preprocessor__categorical_pipeline__encoder__drop | first |
|
| 141 |
+
| preprocessor__categorical_pipeline__encoder__dtype | <class 'numpy.float64'> |
|
| 142 |
+
| preprocessor__categorical_pipeline__encoder__feature_name_combiner | concat |
|
| 143 |
+
| preprocessor__categorical_pipeline__encoder__handle_unknown | infrequent_if_exist |
|
| 144 |
+
| preprocessor__categorical_pipeline__encoder__max_categories | |
|
| 145 |
+
| preprocessor__categorical_pipeline__encoder__min_frequency | |
|
| 146 |
+
| preprocessor__categorical_pipeline__encoder__sparse_output | False |
|
| 147 |
+
| preprocessor__feature_creation_pipeline__memory | |
|
| 148 |
+
| preprocessor__feature_creation_pipeline__steps | [('feature_creation', FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>)), ('imputer', SimpleImputer(strategy='most_frequent')), ('encoder', OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False))] |
|
| 149 |
+
| preprocessor__feature_creation_pipeline__verbose | False |
|
| 150 |
+
| preprocessor__feature_creation_pipeline__feature_creation | FunctionTransformer(func=<function feature_creation at 0x000001E7F14514E0>) |
|
| 151 |
+
| preprocessor__feature_creation_pipeline__imputer | SimpleImputer(strategy='most_frequent') |
|
| 152 |
+
| preprocessor__feature_creation_pipeline__encoder | OneHotEncoder(drop='first', handle_unknown='infrequent_if_exist',<br /> sparse_output=False) |
|
| 153 |
+
| preprocessor__feature_creation_pipeline__feature_creation__accept_sparse | False |
|
| 154 |
+
| preprocessor__feature_creation_pipeline__feature_creation__check_inverse | True |
|
| 155 |
+
| preprocessor__feature_creation_pipeline__feature_creation__feature_names_out | |
|
| 156 |
+
| preprocessor__feature_creation_pipeline__feature_creation__func | <function feature_creation at 0x000001E7F14514E0> |
|
| 157 |
+
| preprocessor__feature_creation_pipeline__feature_creation__inv_kw_args | |
|
| 158 |
+
| preprocessor__feature_creation_pipeline__feature_creation__inverse_func | |
|
| 159 |
+
| preprocessor__feature_creation_pipeline__feature_creation__kw_args | |
|
| 160 |
+
| preprocessor__feature_creation_pipeline__feature_creation__validate | False |
|
| 161 |
+
| preprocessor__feature_creation_pipeline__imputer__add_indicator | False |
|
| 162 |
+
| preprocessor__feature_creation_pipeline__imputer__copy | True |
|
| 163 |
+
| preprocessor__feature_creation_pipeline__imputer__fill_value | |
|
| 164 |
+
| preprocessor__feature_creation_pipeline__imputer__keep_empty_features | False |
|
| 165 |
+
| preprocessor__feature_creation_pipeline__imputer__missing_values | nan |
|
| 166 |
+
| preprocessor__feature_creation_pipeline__imputer__strategy | most_frequent |
|
| 167 |
+
| preprocessor__feature_creation_pipeline__encoder__categories | auto |
|
| 168 |
+
| preprocessor__feature_creation_pipeline__encoder__drop | first |
|
| 169 |
+
| preprocessor__feature_creation_pipeline__encoder__dtype | <class 'numpy.float64'> |
|
| 170 |
+
| preprocessor__feature_creation_pipeline__encoder__feature_name_combiner | concat |
|
| 171 |
+
| preprocessor__feature_creation_pipeline__encoder__handle_unknown | infrequent_if_exist |
|
| 172 |
+
| preprocessor__feature_creation_pipeline__encoder__max_categories | |
|
| 173 |
+
| preprocessor__feature_creation_pipeline__encoder__min_frequency | |
|
| 174 |
+
| preprocessor__feature_creation_pipeline__encoder__sparse_output | False |
|
| 175 |
+
| feature-selection__k | all |
|
| 176 |
+
| feature-selection__score_func | <function mutual_info_classif at 0x000001E7EDA4E480> |
|
| 177 |
+
| classifier__objective | binary:logistic |
|
| 178 |
+
| classifier__base_score | |
|
| 179 |
+
| classifier__booster | |
|
| 180 |
+
| classifier__callbacks | |
|
| 181 |
+
| classifier__colsample_bylevel | |
|
| 182 |
+
| classifier__colsample_bynode | |
|
| 183 |
+
| classifier__colsample_bytree | |
|
| 184 |
+
| classifier__device | |
|
| 185 |
+
| classifier__early_stopping_rounds | |
|
| 186 |
+
| classifier__enable_categorical | False |
|
| 187 |
+
| classifier__eval_metric | |
|
| 188 |
+
| classifier__feature_types | |
|
| 189 |
+
| classifier__gamma | |
|
| 190 |
+
| classifier__grow_policy | |
|
| 191 |
+
| classifier__importance_type | |
|
| 192 |
+
| classifier__interaction_constraints | |
|
| 193 |
+
| classifier__learning_rate | |
|
| 194 |
+
| classifier__max_bin | |
|
| 195 |
+
| classifier__max_cat_threshold | |
|
| 196 |
+
| classifier__max_cat_to_onehot | |
|
| 197 |
+
| classifier__max_delta_step | |
|
| 198 |
+
| classifier__max_depth | 20 |
|
| 199 |
+
| classifier__max_leaves | |
|
| 200 |
+
| classifier__min_child_weight | |
|
| 201 |
+
| classifier__missing | nan |
|
| 202 |
+
| classifier__monotone_constraints | |
|
| 203 |
+
| classifier__multi_strategy | |
|
| 204 |
+
| classifier__n_estimators | 10 |
|
| 205 |
+
| classifier__n_jobs | -1 |
|
| 206 |
+
| classifier__num_parallel_tree | |
|
| 207 |
+
| classifier__random_state | 2024 |
|
| 208 |
+
| classifier__reg_alpha | |
|
| 209 |
+
| classifier__reg_lambda | |
|
| 210 |
+
| classifier__sampling_method | |
|
| 211 |
+
| classifier__scale_pos_weight | |
|
| 212 |
+
| classifier__subsample | |
|
| 213 |
+
| classifier__tree_method | |
|
| 214 |
+
| classifier__validate_parameters | |
|
| 215 |
+
| classifier__verbosity | |
|
| 216 |
+
| classifier__verbose | 0 |
|
| 217 |
+
|
| 218 |
+
</details>
|
| 219 |
+
|
| 220 |
+
### Model Plot
|
| 221 |
+
|
| 222 |
+
<style>#sk-container-id-14 {/* 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;}
|
| 223 |
+
}#sk-container-id-14 {color: var(--sklearn-color-text);
|
| 224 |
+
}#sk-container-id-14 pre {padding: 0;
|
| 225 |
+
}#sk-container-id-14 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;
|
| 226 |
+
}#sk-container-id-14 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);
|
| 227 |
+
}#sk-container-id-14 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;
|
| 228 |
+
}#sk-container-id-14 div.sk-text-repr-fallback {display: none;
|
| 229 |
+
}div.sk-parallel-item,
|
| 230 |
+
div.sk-serial,
|
| 231 |
+
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;
|
| 232 |
+
}/* Parallel-specific style estimator block */#sk-container-id-14 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
|
| 233 |
+
}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
|
| 234 |
+
}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;
|
| 235 |
+
}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
|
| 236 |
+
}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
|
| 237 |
+
}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;
|
| 238 |
+
}/* Serial-specific style estimator block */#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
|
| 239 |
+
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 240 |
+
clickable and can be expanded/collapsed.
|
| 241 |
+
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 242 |
+
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 243 |
+
*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-14 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);
|
| 244 |
+
}/* Toggleable label */
|
| 245 |
+
#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
|
| 246 |
+
}#sk-container-id-14 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);
|
| 247 |
+
}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
|
| 248 |
+
}/* Toggleable content - dropdown */#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
|
| 249 |
+
}#sk-container-id-14 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 250 |
+
}#sk-container-id-14 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);
|
| 251 |
+
}#sk-container-id-14 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 252 |
+
}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
|
| 253 |
+
}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
|
| 254 |
+
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-14 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);
|
| 255 |
+
}#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
|
| 256 |
+
}/* Estimator-specific style *//* Colorize estimator box */
|
| 257 |
+
#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 258 |
+
}#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 259 |
+
}#sk-container-id-14 div.sk-label label.sk-toggleable__label,
|
| 260 |
+
#sk-container-id-14 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
|
| 261 |
+
}/* On hover, darken the color of the background */
|
| 262 |
+
#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
|
| 263 |
+
}/* Label box, darken color on hover, fitted */
|
| 264 |
+
#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
|
| 265 |
+
}/* Estimator label */#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
|
| 266 |
+
}#sk-container-id-14 div.sk-label-container {text-align: center;
|
| 267 |
+
}/* Estimator-specific */
|
| 268 |
+
#sk-container-id-14 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);
|
| 269 |
+
}#sk-container-id-14 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 270 |
+
}/* on hover */
|
| 271 |
+
#sk-container-id-14 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 272 |
+
}#sk-container-id-14 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 273 |
+
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
|
| 274 |
+
a:link.sk-estimator-doc-link,
|
| 275 |
+
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);
|
| 276 |
+
}.sk-estimator-doc-link.fitted,
|
| 277 |
+
a:link.sk-estimator-doc-link.fitted,
|
| 278 |
+
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 279 |
+
}/* On hover */
|
| 280 |
+
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 281 |
+
.sk-estimator-doc-link:hover,
|
| 282 |
+
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 283 |
+
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 284 |
+
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 285 |
+
.sk-estimator-doc-link.fitted:hover,
|
| 286 |
+
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 287 |
+
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 288 |
+
}/* Span, style for the box shown on hovering the info icon */
|
| 289 |
+
.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);
|
| 290 |
+
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
|
| 291 |
+
}.sk-estimator-doc-link:hover span {display: block;
|
| 292 |
+
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-14 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;
|
| 293 |
+
}#sk-container-id-14 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 294 |
+
}/* On hover */
|
| 295 |
+
#sk-container-id-14 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 296 |
+
}#sk-container-id-14 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
|
| 297 |
+
}
|
| 298 |
+
</style><div id="sk-container-id-14" 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...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, ...))])</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-170" type="checkbox" ><label for="sk-estimator-id-170" 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...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, ...))])</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-171" type="checkbox" ><label for="sk-estimator-id-171" 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 0x000001E7F14514E0>)),('imputer',SimpleImputer(strategy='most_frequent')),('encoder',OneHotEncoder(drop='first',handle_unknown='infrequent_if_exist',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-172" type="checkbox" ><label for="sk-estimator-id-172" 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-173" type="checkbox" ><label for="sk-estimator-id-173" 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-174" type="checkbox" ><label for="sk-estimator-id-174" 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-175" type="checkbox" ><label for="sk-estimator-id-175" 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-176" type="checkbox" ><label for="sk-estimator-id-176" 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-177" type="checkbox" ><label for="sk-estimator-id-177" 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 0x000001E7F1450680>)</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-178" type="checkbox" ><label for="sk-estimator-id-178" 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-179" type="checkbox" ><label for="sk-estimator-id-179" 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-180" type="checkbox" ><label for="sk-estimator-id-180" 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-181" type="checkbox" ><label for="sk-estimator-id-181" 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 0x000001E7F14514E0>)</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-182" type="checkbox" ><label for="sk-estimator-id-182" 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-183" type="checkbox" ><label for="sk-estimator-id-183" 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></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-184" type="checkbox" ><label for="sk-estimator-id-184" 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 0x000001E7EDA4E480>)</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-185" type="checkbox" ><label for="sk-estimator-id-185" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">XGBClassifier</label><div class="sk-toggleable__content fitted"><pre>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, ...)</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/XGBClassifier.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 XGBClassifier 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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XGBClassifier.joblib
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:ec332cb84cb397dec64c51e51973d2c562b8b63e39b65598c713b1fd00d379c5
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size 44802
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config.json
ADDED
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{
|
| 2 |
+
"sklearn": {
|
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+
"columns": [
|
| 4 |
+
"id",
|
| 5 |
+
"prg",
|
| 6 |
+
"pl",
|
| 7 |
+
"pr",
|
| 8 |
+
"sk",
|
| 9 |
+
"ts",
|
| 10 |
+
"m11",
|
| 11 |
+
"bd2",
|
| 12 |
+
"age",
|
| 13 |
+
"insurance",
|
| 14 |
+
"sepsis"
|
| 15 |
+
],
|
| 16 |
+
"environment": [
|
| 17 |
+
"scikit-learn=1.5.0",
|
| 18 |
+
"imbalanced-learn=0.12.3"
|
| 19 |
+
],
|
| 20 |
+
"example_input": {
|
| 21 |
+
"age": [
|
| 22 |
+
50,
|
| 23 |
+
31,
|
| 24 |
+
32
|
| 25 |
+
],
|
| 26 |
+
"bd2": [
|
| 27 |
+
0.627,
|
| 28 |
+
0.351,
|
| 29 |
+
0.672
|
| 30 |
+
],
|
| 31 |
+
"id": [
|
| 32 |
+
"ICU200010",
|
| 33 |
+
"ICU200011",
|
| 34 |
+
"ICU200012"
|
| 35 |
+
],
|
| 36 |
+
"insurance": [
|
| 37 |
+
0,
|
| 38 |
+
0,
|
| 39 |
+
1
|
| 40 |
+
],
|
| 41 |
+
"m11": [
|
| 42 |
+
33.6,
|
| 43 |
+
26.6,
|
| 44 |
+
23.3
|
| 45 |
+
],
|
| 46 |
+
"pl": [
|
| 47 |
+
148,
|
| 48 |
+
85,
|
| 49 |
+
183
|
| 50 |
+
],
|
| 51 |
+
"pr": [
|
| 52 |
+
72,
|
| 53 |
+
66,
|
| 54 |
+
64
|
| 55 |
+
],
|
| 56 |
+
"prg": [
|
| 57 |
+
6,
|
| 58 |
+
1,
|
| 59 |
+
8
|
| 60 |
+
],
|
| 61 |
+
"sepsis": [
|
| 62 |
+
"Positive",
|
| 63 |
+
"Negative",
|
| 64 |
+
"Positive"
|
| 65 |
+
],
|
| 66 |
+
"sk": [
|
| 67 |
+
35,
|
| 68 |
+
29,
|
| 69 |
+
0
|
| 70 |
+
],
|
| 71 |
+
"ts": [
|
| 72 |
+
0,
|
| 73 |
+
0,
|
| 74 |
+
0
|
| 75 |
+
]
|
| 76 |
+
},
|
| 77 |
+
"model": {
|
| 78 |
+
"file": "XGBClassifier.joblib"
|
| 79 |
+
},
|
| 80 |
+
"model_format": "pickle",
|
| 81 |
+
"task": "tabular-classification"
|
| 82 |
+
}
|
| 83 |
+
}
|