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--- |
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license: mit |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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model_format: pickle |
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model_file: LightGBM_without_hospital_number_01.pkl |
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widget: |
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structuredData: |
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abdomen: |
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- distend_small |
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- distend_small |
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- distend_large |
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abdominal_distention: |
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- none |
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- none |
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- moderate |
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abdomo_appearance: |
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- serosanguious |
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- cloudy |
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- serosanguious |
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abdomo_protein: |
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- 4.1 |
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- 4.3 |
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- 2.0 |
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age: |
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- adult |
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- adult |
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- adult |
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capillary_refill_time: |
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- less_3_sec |
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- less_3_sec |
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- more_3_sec |
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cp_data: |
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- 'yes' |
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- 'yes' |
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- 'no' |
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lesion_1: |
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- 7209 |
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- 2112 |
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- 5400 |
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lesion_2: |
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- 0 |
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- 0 |
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- 0 |
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lesion_3: |
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- 0 |
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- 0 |
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- 0 |
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mucous_membrane: |
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- bright_pink |
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- bright_pink |
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- dark_cyanotic |
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nasogastric_reflux: |
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- none |
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- none |
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- more_1_liter |
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nasogastric_reflux_ph: |
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- 7.0 |
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- 3.5 |
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- 2.0 |
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nasogastric_tube: |
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- slight |
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- none |
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- significant |
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packed_cell_volume: |
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- 37.0 |
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- 44.0 |
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- 65.0 |
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pain: |
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- depressed |
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- mild_pain |
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- extreme_pain |
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peripheral_pulse: |
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- normal |
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- normal |
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- reduced |
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peristalsis: |
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- hypermotile |
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- hypomotile |
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- absent |
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pulse: |
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- 84.0 |
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- 66.0 |
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- 72.0 |
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rectal_exam_feces: |
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- absent |
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- decreased |
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- absent |
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rectal_temp: |
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- 39.0 |
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- 38.5 |
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- 37.3 |
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respiratory_rate: |
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- 24.0 |
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- 21.0 |
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- 30.0 |
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surgery: |
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- 'yes' |
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- 'yes' |
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- 'yes' |
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surgical_lesion: |
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- 'yes' |
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- 'yes' |
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- 'yes' |
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temp_of_extremities: |
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- cool |
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- normal |
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- cool |
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total_protein: |
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- 6.5 |
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- 7.6 |
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- 13.0 |
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--- |
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# Model description |
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This is a `LightGBM` model trained on horse health outcome data from Kaggle. |
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## Intended uses & limitations |
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This model is not ready to be used in production. |
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## Training Procedure |
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[More Information Needed] |
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### Hyperparameters |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|--------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------| |
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| memory | | |
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| steps | [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])])), ('classifier', LGBMClassifier(max_depth=3))] | |
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| verbose | False | |
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| preprocessor | ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])]) | |
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| classifier | LGBMClassifier(max_depth=3) | |
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| preprocessor__n_jobs | | |
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| preprocessor__remainder | passthrough | |
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| preprocessor__sparse_threshold | 0.3 | |
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| preprocessor__transformer_weights | | |
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| preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])] | |
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| preprocessor__verbose | False | |
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| preprocessor__verbose_feature_names_out | True | |
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| preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]) | |
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| preprocessor__cat | Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]) | |
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| preprocessor__num__memory | | |
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| preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())] | |
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| preprocessor__num__verbose | False | |
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| preprocessor__num__imputer | SimpleImputer(strategy='median') | |
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| preprocessor__num__scaler | StandardScaler() | |
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| preprocessor__num__imputer__add_indicator | False | |
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| preprocessor__num__imputer__copy | True | |
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| preprocessor__num__imputer__fill_value | | |
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| preprocessor__num__imputer__keep_empty_features | False | |
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| preprocessor__num__imputer__missing_values | nan | |
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| preprocessor__num__imputer__strategy | median | |
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| preprocessor__num__scaler__copy | True | |
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| preprocessor__num__scaler__with_mean | True | |
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| preprocessor__num__scaler__with_std | True | |
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| preprocessor__cat__memory | | |
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| preprocessor__cat__steps | [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))] | |
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| preprocessor__cat__verbose | False | |
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| preprocessor__cat__imputer | SimpleImputer(fill_value='missing', strategy='constant') | |
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| preprocessor__cat__onehot | OneHotEncoder(handle_unknown='ignore') | |
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| preprocessor__cat__imputer__add_indicator | False | |
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| preprocessor__cat__imputer__copy | True | |
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| preprocessor__cat__imputer__fill_value | missing | |
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| preprocessor__cat__imputer__keep_empty_features | False | |
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| preprocessor__cat__imputer__missing_values | nan | |
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| preprocessor__cat__imputer__strategy | constant | |
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| preprocessor__cat__onehot__categories | auto | |
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| preprocessor__cat__onehot__drop | | |
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| preprocessor__cat__onehot__dtype | <class 'numpy.float64'> | |
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| preprocessor__cat__onehot__feature_name_combiner | concat | |
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| preprocessor__cat__onehot__handle_unknown | ignore | |
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| preprocessor__cat__onehot__max_categories | | |
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| preprocessor__cat__onehot__min_frequency | | |
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| preprocessor__cat__onehot__sparse | deprecated | |
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| preprocessor__cat__onehot__sparse_output | True | |
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| classifier__boosting_type | gbdt | |
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| classifier__class_weight | | |
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| classifier__colsample_bytree | 1.0 | |
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| classifier__importance_type | split | |
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| classifier__learning_rate | 0.1 | |
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| classifier__max_depth | 3 | |
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| classifier__min_child_samples | 20 | |
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| classifier__min_child_weight | 0.001 | |
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| classifier__min_split_gain | 0.0 | |
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| classifier__n_estimators | 100 | |
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| classifier__n_jobs | | |
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| classifier__num_leaves | 31 | |
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| classifier__objective | | |
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| classifier__random_state | | |
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| classifier__reg_alpha | 0.0 | |
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| classifier__reg_lambda | 0.0 | |
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| classifier__subsample | 1.0 | |
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| classifier__subsample_for_bin | 200000 | |
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| classifier__subsample_freq | 0 | |
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</details> |
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### Model Plot |
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<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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 the default 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;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier', LGBMClassifier(max_depth=3))])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier', LGBMClassifier(max_depth=3))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),['rectal_temp', 'pulse', 'respiratory_rate','nasogastric_reflux_ph', 'packed_cell_volume','total_protein', 'abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pipeline(steps=[('imputer',SimpleI...='missing',strategy='constant')),('onehot',OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age', 'temp_of_extremities','peripheral_pulse', 'mucous_membrane','capillary_refill_time', 'pain','peristalsis', 'abdominal_distention','nasogastric_tube', 'nasogastric_reflux','rectal_exam_feces', 'abdomen','abdomo_appearance', 'surgical_lesion','cp_data'])])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-30" type="checkbox" ><label for="sk-estimator-id-30" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-31" type="checkbox" ><label for="sk-estimator-id-31" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-32" type="checkbox" ><label for="sk-estimator-id-32" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMClassifier</label><div class="sk-toggleable__content"><pre>LGBMClassifier(max_depth=3)</pre></div></div></div></div></div></div></div> |
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## Evaluation Results |
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| Metric | Value | |
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|----------|----------| |
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| accuracy | 0.740891 | |
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| f1 score | 0.740891 | |
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### Confusion Matrix |
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## Permutation Importance |
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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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kmposkid |
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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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