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
<details> <summary> Click to expand </summary>
Hyperparameter | Value |
---|---|
memory | |
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))] |
verbose | False |
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) |
preprocessor__n_jobs | |
preprocessor__remainder | passthrough |
preprocessor__sparse_threshold | 0.3 |
preprocessor__transformer_weights | |
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'])] |
preprocessor__verbose | False |
preprocessor__verbose_feature_names_out | True |
preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]) |
preprocessor__cat | Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('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 |
</details>
Model Plot
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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>
Evaluation Results
Metric | Value |
---|---|
accuracy | 0.740891 |
f1 score | 0.740891 |
Confusion Matrix
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Model Card Authors
kmposkid
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