sklearn skops tabular-classification license:mit region:us
---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: LightGBM_without_hospital_number_01.pkl
widget:
  structuredData:
    abdomen:
    - distend_small
    - distend_small
    - distend_large
    abdominal_distention:
    - none
    - none
    - moderate
    abdomo_appearance:
    - serosanguious
    - cloudy
    - serosanguious
    abdomo_protein:
    - 4.1
    - 4.3
    - 2.0
    age:
    - adult
    - adult
    - adult
    capillary_refill_time:
    - less_3_sec
    - less_3_sec
    - more_3_sec
    cp_data:
    - 'yes'
    - 'yes'
    - 'no'
    lesion_1:
    - 7209
    - 2112
    - 5400
    lesion_2:
    - 0
    - 0
    - 0
    lesion_3:
    - 0
    - 0
    - 0
    mucous_membrane:
    - bright_pink
    - bright_pink
    - dark_cyanotic
    nasogastric_reflux:
    - none
    - none
    - more_1_liter
    nasogastric_reflux_ph:
    - 7.0
    - 3.5
    - 2.0
    nasogastric_tube:
    - slight
    - none
    - significant
    packed_cell_volume:
    - 37.0
    - 44.0
    - 65.0
    pain:
    - depressed
    - mild_pain
    - extreme_pain
    peripheral_pulse:
    - normal
    - normal
    - reduced
    peristalsis:
    - hypermotile
    - hypomotile
    - absent
    pulse:
    - 84.0
    - 66.0
    - 72.0
    rectal_exam_feces:
    - absent
    - decreased
    - absent
    rectal_temp:
    - 39.0
    - 38.5
    - 37.3
    respiratory_rate:
    - 24.0
    - 21.0
    - 30.0
    surgery:
    - 'yes'
    - 'yes'
    - 'yes'
    surgical_lesion:
    - 'yes'
    - 'yes'
    - 'yes'
    temp_of_extremities:
    - cool
    - normal
    - cool
    total_protein:
    - 6.5
    - 7.6
    - 13.0
---

# 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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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=[(&#x27;preprocessor&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,StandardScaler())]),[&#x27;rectal_temp&#x27;, &#x27;pulse&#x27;,&#x27;respiratory_rate&#x27;,&#x27;nasogastric_reflux_ph&#x27;,&#x27;packed_cell_volume&#x27;,&#x27;total_protein&#x27;,&#x27;abdomo_protein&#x27;, &#x27;lesion_1&#x27;,&#x27;lesion_2&#x27;, &#x27;lesion_3&#x27;]),(&#x27;cat&#x27;,Pi...OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),[&#x27;surgery&#x27;, &#x27;age&#x27;,&#x27;temp_of_extremities&#x27;,&#x27;peripheral_pulse&#x27;,&#x27;mucous_membrane&#x27;,&#x27;capillary_refill_time&#x27;,&#x27;pain&#x27;, &#x27;peristalsis&#x27;,&#x27;abdominal_distention&#x27;,&#x27;nasogastric_tube&#x27;,&#x27;nasogastric_reflux&#x27;,&#x27;rectal_exam_feces&#x27;,&#x27;abdomen&#x27;,&#x27;abdomo_appearance&#x27;,&#x27;surgical_lesion&#x27;,&#x27;cp_data&#x27;])])),(&#x27;classifier&#x27;, 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=[(&#x27;preprocessor&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;,StandardScaler())]),[&#x27;rectal_temp&#x27;, &#x27;pulse&#x27;,&#x27;respiratory_rate&#x27;,&#x27;nasogastric_reflux_ph&#x27;,&#x27;packed_cell_volume&#x27;,&#x27;total_protein&#x27;,&#x27;abdomo_protein&#x27;, &#x27;lesion_1&#x27;,&#x27;lesion_2&#x27;, &#x27;lesion_3&#x27;]),(&#x27;cat&#x27;,Pi...OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),[&#x27;surgery&#x27;, &#x27;age&#x27;,&#x27;temp_of_extremities&#x27;,&#x27;peripheral_pulse&#x27;,&#x27;mucous_membrane&#x27;,&#x27;capillary_refill_time&#x27;,&#x27;pain&#x27;, &#x27;peristalsis&#x27;,&#x27;abdominal_distention&#x27;,&#x27;nasogastric_tube&#x27;,&#x27;nasogastric_reflux&#x27;,&#x27;rectal_exam_feces&#x27;,&#x27;abdomen&#x27;,&#x27;abdomo_appearance&#x27;,&#x27;surgical_lesion&#x27;,&#x27;cp_data&#x27;])])),(&#x27;classifier&#x27;, 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=&#x27;passthrough&#x27;,transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;, StandardScaler())]),[&#x27;rectal_temp&#x27;, &#x27;pulse&#x27;, &#x27;respiratory_rate&#x27;,&#x27;nasogastric_reflux_ph&#x27;, &#x27;packed_cell_volume&#x27;,&#x27;total_protein&#x27;, &#x27;abdomo_protein&#x27;, &#x27;lesion_1&#x27;,&#x27;lesion_2&#x27;, &#x27;lesion_3&#x27;]),(&#x27;cat&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleI...=&#x27;missing&#x27;,strategy=&#x27;constant&#x27;)),(&#x27;onehot&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),[&#x27;surgery&#x27;, &#x27;age&#x27;, &#x27;temp_of_extremities&#x27;,&#x27;peripheral_pulse&#x27;, &#x27;mucous_membrane&#x27;,&#x27;capillary_refill_time&#x27;, &#x27;pain&#x27;,&#x27;peristalsis&#x27;, &#x27;abdominal_distention&#x27;,&#x27;nasogastric_tube&#x27;, &#x27;nasogastric_reflux&#x27;,&#x27;rectal_exam_feces&#x27;, &#x27;abdomen&#x27;,&#x27;abdomo_appearance&#x27;, &#x27;surgical_lesion&#x27;,&#x27;cp_data&#x27;])])</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>[&#x27;rectal_temp&#x27;, &#x27;pulse&#x27;, &#x27;respiratory_rate&#x27;, &#x27;nasogastric_reflux_ph&#x27;, &#x27;packed_cell_volume&#x27;, &#x27;total_protein&#x27;, &#x27;abdomo_protein&#x27;, &#x27;lesion_1&#x27;, &#x27;lesion_2&#x27;, &#x27;lesion_3&#x27;]</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=&#x27;median&#x27;)</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>[&#x27;surgery&#x27;, &#x27;age&#x27;, &#x27;temp_of_extremities&#x27;, &#x27;peripheral_pulse&#x27;, &#x27;mucous_membrane&#x27;, &#x27;capillary_refill_time&#x27;, &#x27;pain&#x27;, &#x27;peristalsis&#x27;, &#x27;abdominal_distention&#x27;, &#x27;nasogastric_tube&#x27;, &#x27;nasogastric_reflux&#x27;, &#x27;rectal_exam_feces&#x27;, &#x27;abdomen&#x27;, &#x27;abdomo_appearance&#x27;, &#x27;surgical_lesion&#x27;, &#x27;cp_data&#x27;]</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=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</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=&#x27;ignore&#x27;)</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

![Confusion Matrix](confusion_matrix.png)


## Permutation Importance

![Permutation Importance](feature_importance.png)

# How to Get Started with the Model

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# Model Card Authors

kmposkid

# Model Card Contact

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# Citation

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**BibTeX:**
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