Model description
This is a LightGBM model trained on horse health outcome data from Kaggle.
Intended uses & limitations
This model is not ready to be used in production.
Training Procedure
[More Information Needed]
Hyperparameters
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| Hyperparameter | Value |
|---|---|
| memory | |
| 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', 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'])])), ('classifier', LGBMClassifier(max_depth=3))] |
| verbose | False |
| 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', 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'])]) |
| classifier | LGBMClassifier(max_depth=3) |
| preprocessor__n_jobs | |
| preprocessor__remainder | passthrough |
| preprocessor__sparse_threshold | 0.3 |
| preprocessor__transformer_weights | |
| preprocessor__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', SimpleImputer(fill_value='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'])] |
| preprocessor__verbose | False |
| preprocessor__verbose_feature_names_out | True |
| preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) |
| preprocessor__cat | Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))]) |
| preprocessor__num__memory | |
| preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())] |
| preprocessor__num__verbose | False |
| preprocessor__num__imputer | SimpleImputer(strategy='median') |
| preprocessor__num__scaler | StandardScaler() |
| preprocessor__num__imputer__add_indicator | False |
| preprocessor__num__imputer__copy | True |
| preprocessor__num__imputer__fill_value | |
| preprocessor__num__imputer__keep_empty_features | False |
| preprocessor__num__imputer__missing_values | nan |
| preprocessor__num__imputer__strategy | median |
| preprocessor__num__scaler__copy | True |
| preprocessor__num__scaler__with_mean | True |
| preprocessor__num__scaler__with_std | True |
| preprocessor__cat__memory | |
| preprocessor__cat__steps | [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))] |
| preprocessor__cat__verbose | False |
| preprocessor__cat__imputer | SimpleImputer(fill_value='missing', strategy='constant') |
| preprocessor__cat__onehot | OneHotEncoder(handle_unknown='ignore') |
| preprocessor__cat__imputer__add_indicator | False |
| preprocessor__cat__imputer__copy | True |
| preprocessor__cat__imputer__fill_value | missing |
| preprocessor__cat__imputer__keep_empty_features | False |
| preprocessor__cat__imputer__missing_values | nan |
| preprocessor__cat__imputer__strategy | constant |
| preprocessor__cat__onehot__categories | auto |
| preprocessor__cat__onehot__drop | |
| preprocessor__cat__onehot__dtype | <class 'numpy.float64'> |
| preprocessor__cat__onehot__feature_name_combiner | concat |
| preprocessor__cat__onehot__handle_unknown | ignore |
| preprocessor__cat__onehot__max_categories | |
| preprocessor__cat__onehot__min_frequency | |
| preprocessor__cat__onehot__sparse | deprecated |
| preprocessor__cat__onehot__sparse_output | True |
| classifier__boosting_type | gbdt |
| classifier__class_weight | |
| classifier__colsample_bytree | 1.0 |
| classifier__importance_type | split |
| classifier__learning_rate | 0.1 |
| classifier__max_depth | 3 |
| classifier__min_child_samples | 20 |
| classifier__min_child_weight | 0.001 |
| classifier__min_split_gain | 0.0 |
| classifier__n_estimators | 100 |
| classifier__n_jobs | |
| classifier__num_leaves | 31 |
| classifier__objective | |
| classifier__random_state | |
| classifier__reg_alpha | 0.0 |
| classifier__reg_lambda | 0.0 |
| classifier__subsample | 1.0 |
| classifier__subsample_for_bin | 200000 |
| classifier__subsample_freq | 0 |
Model Plot
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))])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.
Pipeline
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))])preprocessor: ColumnTransformer
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'])])num
['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']
SimpleImputer
SimpleImputer(strategy='median')
StandardScaler
StandardScaler()
cat
['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']
SimpleImputer
SimpleImputer(fill_value='missing', strategy='constant')
OneHotEncoder
OneHotEncoder(handle_unknown='ignore')
remainder
[]
passthrough
passthrough
LGBMClassifier
LGBMClassifier(max_depth=3)
Evaluation Results
| Metric | Value |
|---|---|
| accuracy | 0.740891 |
| f1 score | 0.740891 |
Confusion Matrix
Permutation Importance
How to Get Started with the Model
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Model Card Authors
kmposkid
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