Edit model card
YAML Metadata Error: "widget" must be an array

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

Click to expand
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.

Evaluation Results

Metric Value
accuracy 0.740891
f1 score 0.740891

Confusion Matrix

Confusion Matrix

Permutation Importance

Permutation Importance

How to Get Started with the Model

[More Information Needed]

Model Card Authors

kmposkid

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

[More Information Needed]
Downloads last month
0
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.