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

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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