---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: pipeline_model_sklearn.joblib
widget:
- structuredData:
    Age:
    - 23
    - 47
    - 47
    BP:
    - HIGH
    - LOW
    - LOW
    Cholesterol:
    - HIGH
    - HIGH
    - HIGH
    K:
    - 0.031258
    - 0.056468
    - 0.068944
    Na:
    - 0.792535
    - 0.739309
    - 0.697269
    Sex:
    - F
    - M
    - M
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

[More Information Needed]

### Hyperparameters

<details>
<summary> Click to expand </summary>

| Hyperparameter                                                                                            | Value                                                                                                                                                                                                                                                                                              |
|-----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| memory                                                                                                    |                                                                                                                                                                                                                                                                                                    |
| steps                                                                                                     | [('featureunion', FeatureUnion(transformer_list=[('float32_transform_139955258811312',<br />                                Pipeline(steps=[('numpycolumnselector',<br />                                                 NumpyColumnSelector(columns=[1,<br />                                                                              2,<br />                                                                              3])),<br />                                                ('compressstrings',<br />                                                 CompressStrings(compress_type='hash',<br />                                                                 dtypes_list=['char_str',<br />                                                                              'char_str',<br />                                                                              'char_str'],<br />                                                                 missing_values_reference_list=['',<br />                                                                                                '-',<br />                                                                                                '?',<br />                                                                                                nan],<br />                                                                 misslist_list=[[],<br />                                                                                [],<br />                                                                                []])),<br />                                                ('numpyreplacemissingvalues'...<br />                                                 FloatStr2Float(dtypes_list=['float_int_num',<br />                                                                             'float_num',<br />                                                                             'float_num'],<br />                                                                missing_values_reference_list=[])),<br />                                                ('numpyreplacemissingvalues',<br />                                                 NumpyReplaceMissingValues(missing_values=[])),<br />                                                ('numimputer',<br />                                                 NumImputer(missing_values=nan,<br />                                                            strategy='median')),<br />                                                ('optstandardscaler',<br />                                                 OptStandardScaler(use_scaler_flag=False)),<br />                                                ('float32_transform',<br />                                                 float32_transform())]))])), ('numpypermutearray', NumpyPermuteArray(axis=0, permutation_indices=[1, 2, 3, 0, 4, 5])), ('lgbmclassifier', LGBMClassifier(class_weight='balanced', n_jobs=1, random_state=33))]                                                                                                                                                                                                                                                                                                    |
| verbose                                                                                                   | False                                                                                                                                                                                                                                                                                              |
| featureunion                                                                                              | FeatureUnion(transformer_list=[('float32_transform_139955258811312',<br />                                Pipeline(steps=[('numpycolumnselector',<br />                                                 NumpyColumnSelector(columns=[1,<br />                                                                              2,<br />                                                                              3])),<br />                                                ('compressstrings',<br />                                                 CompressStrings(compress_type='hash',<br />                                                                 dtypes_list=['char_str',<br />                                                                              'char_str',<br />                                                                              'char_str'],<br />                                                                 missing_values_reference_list=['',<br />                                                                                                '-',<br />                                                                                                '?',<br />                                                                                                nan],<br />                                                                 misslist_list=[[],<br />                                                                                [],<br />                                                                                []])),<br />                                                ('numpyreplacemissingvalues'...<br />                                                 FloatStr2Float(dtypes_list=['float_int_num',<br />                                                                             'float_num',<br />                                                                             'float_num'],<br />                                                                missing_values_reference_list=[])),<br />                                                ('numpyreplacemissingvalues',<br />                                                 NumpyReplaceMissingValues(missing_values=[])),<br />                                                ('numimputer',<br />                                                 NumImputer(missing_values=nan,<br />                                                            strategy='median')),<br />                                                ('optstandardscaler',<br />                                                 OptStandardScaler(use_scaler_flag=False)),<br />                                                ('float32_transform',<br />                                                 float32_transform())]))])                                                                                                                                                                                                                                                                                                    |
| numpypermutearray                                                                                         | NumpyPermuteArray(axis=0, permutation_indices=[1, 2, 3, 0, 4, 5])                                                                                                                                                                                                                                  |
| lgbmclassifier                                                                                            | LGBMClassifier(class_weight='balanced', n_jobs=1, random_state=33)                                                                                                                                                                                                                                 |
| featureunion__n_jobs                                                                                      |                                                                                                                                                                                                                                                                                                    |
| featureunion__transformer_list                                                                            | [('float32_transform_139955258811312', Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[1, 2, 3])),<br />                ('compressstrings',<br />                 CompressStrings(compress_type='hash',<br />                                 dtypes_list=['char_str', 'char_str',<br />                                              'char_str'],<br />                                 missing_values_reference_list=['', '-', '?',<br />                                                                nan],<br />                                 misslist_list=[[], [], []])),<br />                ('numpyreplacemissingvalues',<br />                 NumpyReplaceMissingValues(missing_values=[])),<br />                ('numpyreplaceunknown...<br />                                                               40061271003327253395033901872323469393]],<br />                                           missing_values_reference_list=['',<br />                                                                          '-',<br />                                                                          '?',<br />                                                                          nan])),<br />                ('boolean2float', boolean2float()),<br />                ('catimputer',<br />                 CatImputer(missing_values=nan, strategy='most_frequent')),<br />                ('catencoder',<br />                 CatEncoder(categories='auto', dtype=<class 'numpy.float64'>,<br />                            encoding='ordinal', handle_unknown='error')),<br />                ('float32_transform', float32_transform())])), ('float32_transform_139955258809968', Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[0, 4, 5])),<br />                ('floatstr2float',<br />                 FloatStr2Float(dtypes_list=['float_int_num', 'float_num',<br />                                             'float_num'],<br />                                missing_values_reference_list=[])),<br />                ('numpyreplacemissingvalues',<br />                 NumpyReplaceMissingValues(missing_values=[])),<br />                ('numimputer',<br />                 NumImputer(missing_values=nan, strategy='median')),<br />                ('optstandardscaler', OptStandardScaler(use_scaler_flag=False)),<br />                ('float32_transform', float32_transform())]))]                                                                                                                                                                                                                                                                                                    |
| featureunion__transformer_weights                                                                         |                                                                                                                                                                                                                                                                                                    |
| featureunion__verbose                                                                                     | False                                                                                                                                                                                                                                                                                              |
| featureunion__float32_transform_139955258811312                                                           | Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[1, 2, 3])),<br />                ('compressstrings',<br />                 CompressStrings(compress_type='hash',<br />                                 dtypes_list=['char_str', 'char_str',<br />                                              'char_str'],<br />                                 missing_values_reference_list=['', '-', '?',<br />                                                                nan],<br />                                 misslist_list=[[], [], []])),<br />                ('numpyreplacemissingvalues',<br />                 NumpyReplaceMissingValues(missing_values=[])),<br />                ('numpyreplaceunknown...<br />                                                               40061271003327253395033901872323469393]],<br />                                           missing_values_reference_list=['',<br />                                                                          '-',<br />                                                                          '?',<br />                                                                          nan])),<br />                ('boolean2float', boolean2float()),<br />                ('catimputer',<br />                 CatImputer(missing_values=nan, strategy='most_frequent')),<br />                ('catencoder',<br />                 CatEncoder(categories='auto', dtype=<class 'numpy.float64'>,<br />                            encoding='ordinal', handle_unknown='error')),<br />                ('float32_transform', float32_transform())])                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258809968                                                           | Pipeline(steps=[('numpycolumnselector', NumpyColumnSelector(columns=[0, 4, 5])),<br />                ('floatstr2float',<br />                 FloatStr2Float(dtypes_list=['float_int_num', 'float_num',<br />                                             'float_num'],<br />                                missing_values_reference_list=[])),<br />                ('numpyreplacemissingvalues',<br />                 NumpyReplaceMissingValues(missing_values=[])),<br />                ('numimputer',<br />                 NumImputer(missing_values=nan, strategy='median')),<br />                ('optstandardscaler', OptStandardScaler(use_scaler_flag=False)),<br />                ('float32_transform', float32_transform())])                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__memory                                                   |                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__steps                                                    | [('numpycolumnselector', NumpyColumnSelector(columns=[1, 2, 3])), ('compressstrings', CompressStrings(compress_type='hash',<br />                dtypes_list=['char_str', 'char_str', 'char_str'],<br />                missing_values_reference_list=['', '-', '?', nan],<br />                misslist_list=[[], [], []])), ('numpyreplacemissingvalues', NumpyReplaceMissingValues(missing_values=[])), ('numpyreplaceunknownvalues', NumpyReplaceUnknownValues(filling_values=nan,<br />                          filling_values_list=[nan, nan, nan],<br />                          known_values_list=[[170172835760119224333519554008280666130,<br />                                              140114708448418632577632402066430035116],<br />                                             [245397760256243238036686602120338271372,<br />                                              87378989482499796866217412016778320776,<br />                                              40061271003327253395033901872323469393],<br />                                             [245397760256243238036686602120338271372,<br />                                              40061271003327253395033901872323469393]],<br />                          missing_values_reference_list=['', '-', '?', nan])), ('boolean2float', boolean2float()), ('catimputer', CatImputer(missing_values=nan, strategy='most_frequent')), ('catencoder', CatEncoder(categories='auto', dtype=<class 'numpy.float64'>, encoding='ordinal',<br />           handle_unknown='error')), ('float32_transform', float32_transform())]                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__verbose                                                  | False                                                                                                                                                                                                                                                                                              |
| featureunion__float32_transform_139955258811312__numpycolumnselector                                      | NumpyColumnSelector(columns=[1, 2, 3])                                                                                                                                                                                                                                                             |
| featureunion__float32_transform_139955258811312__compressstrings                                          | CompressStrings(compress_type='hash',<br />                dtypes_list=['char_str', 'char_str', 'char_str'],<br />                missing_values_reference_list=['', '-', '?', nan],<br />                misslist_list=[[], [], []])                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues                                | NumpyReplaceMissingValues(missing_values=[])                                                                                                                                                                                                                                                       |
| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues                                | NumpyReplaceUnknownValues(filling_values=nan,<br />                          filling_values_list=[nan, nan, nan],<br />                          known_values_list=[[170172835760119224333519554008280666130,<br />                                              140114708448418632577632402066430035116],<br />                                             [245397760256243238036686602120338271372,<br />                                              87378989482499796866217412016778320776,<br />                                              40061271003327253395033901872323469393],<br />                                             [245397760256243238036686602120338271372,<br />                                              40061271003327253395033901872323469393]],<br />                          missing_values_reference_list=['', '-', '?', nan])                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__boolean2float                                            | boolean2float()                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__catimputer                                               | CatImputer(missing_values=nan, strategy='most_frequent')                                                                                                                                                                                                                                           |
| featureunion__float32_transform_139955258811312__catencoder                                               | CatEncoder(categories='auto', dtype=<class 'numpy.float64'>, encoding='ordinal',<br />           handle_unknown='error')                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__float32_transform                                        | float32_transform()                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258811312__numpycolumnselector__columns                             | [1, 2, 3]                                                                                                                                                                                                                                                                                          |
| featureunion__float32_transform_139955258811312__compressstrings__activate_flag                           | True                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258811312__compressstrings__compress_type                           | hash                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258811312__compressstrings__dtypes_list                             | ['char_str', 'char_str', 'char_str']                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258811312__compressstrings__missing_values_reference_list           | ['', '-', '?', nan]                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258811312__compressstrings__misslist_list                           | [[], [], []]                                                                                                                                                                                                                                                                                       |
| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues__filling_values                | nan                                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues__missing_values                | []                                                                                                                                                                                                                                                                                                 |
| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__filling_values                | nan                                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__filling_values_list           | [nan, nan, nan]                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__known_values_list             | [[170172835760119224333519554008280666130, 140114708448418632577632402066430035116], [245397760256243238036686602120338271372, 87378989482499796866217412016778320776, 40061271003327253395033901872323469393], [245397760256243238036686602120338271372, 40061271003327253395033901872323469393]] |
| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__missing_values_reference_list | ['', '-', '?', nan]                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258811312__boolean2float__activate_flag                             | True                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258811312__catimputer__activate_flag                                | True                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258811312__catimputer__missing_values                               | nan                                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258811312__catimputer__sklearn_version_family                       | 1                                                                                                                                                                                                                                                                                                  |
| featureunion__float32_transform_139955258811312__catimputer__strategy                                     | most_frequent                                                                                                                                                                                                                                                                                      |
| featureunion__float32_transform_139955258811312__catencoder__activate_flag                                | True                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258811312__catencoder__categories                                   | auto                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258811312__catencoder__dtype                                        | <class 'numpy.float64'>                                                                                                                                                                                                                                                                            |
| featureunion__float32_transform_139955258811312__catencoder__encoding                                     | ordinal                                                                                                                                                                                                                                                                                            |
| featureunion__float32_transform_139955258811312__catencoder__handle_unknown                               | error                                                                                                                                                                                                                                                                                              |
| featureunion__float32_transform_139955258811312__catencoder__sklearn_version_family                       | 1                                                                                                                                                                                                                                                                                                  |
| featureunion__float32_transform_139955258811312__float32_transform__activate_flag                         | True                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258809968__memory                                                   |                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258809968__steps                                                    | [('numpycolumnselector', NumpyColumnSelector(columns=[0, 4, 5])), ('floatstr2float', FloatStr2Float(dtypes_list=['float_int_num', 'float_num', 'float_num'],<br />               missing_values_reference_list=[])), ('numpyreplacemissingvalues', NumpyReplaceMissingValues(missing_values=[])), ('numimputer', NumImputer(missing_values=nan, strategy='median')), ('optstandardscaler', OptStandardScaler(use_scaler_flag=False)), ('float32_transform', float32_transform())]                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258809968__verbose                                                  | False                                                                                                                                                                                                                                                                                              |
| featureunion__float32_transform_139955258809968__numpycolumnselector                                      | NumpyColumnSelector(columns=[0, 4, 5])                                                                                                                                                                                                                                                             |
| featureunion__float32_transform_139955258809968__floatstr2float                                           | FloatStr2Float(dtypes_list=['float_int_num', 'float_num', 'float_num'],<br />               missing_values_reference_list=[])                                                                                                                                                                                                                                                                                                    |
| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues                                | NumpyReplaceMissingValues(missing_values=[])                                                                                                                                                                                                                                                       |
| featureunion__float32_transform_139955258809968__numimputer                                               | NumImputer(missing_values=nan, strategy='median')                                                                                                                                                                                                                                                  |
| featureunion__float32_transform_139955258809968__optstandardscaler                                        | OptStandardScaler(use_scaler_flag=False)                                                                                                                                                                                                                                                           |
| featureunion__float32_transform_139955258809968__float32_transform                                        | float32_transform()                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258809968__numpycolumnselector__columns                             | [0, 4, 5]                                                                                                                                                                                                                                                                                          |
| featureunion__float32_transform_139955258809968__floatstr2float__activate_flag                            | True                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258809968__floatstr2float__dtypes_list                              | ['float_int_num', 'float_num', 'float_num']                                                                                                                                                                                                                                                        |
| featureunion__float32_transform_139955258809968__floatstr2float__missing_values_reference_list            | []                                                                                                                                                                                                                                                                                                 |
| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues__filling_values                | nan                                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues__missing_values                | []                                                                                                                                                                                                                                                                                                 |
| featureunion__float32_transform_139955258809968__numimputer__activate_flag                                | True                                                                                                                                                                                                                                                                                               |
| featureunion__float32_transform_139955258809968__numimputer__missing_values                               | nan                                                                                                                                                                                                                                                                                                |
| featureunion__float32_transform_139955258809968__numimputer__strategy                                     | median                                                                                                                                                                                                                                                                                             |
| featureunion__float32_transform_139955258809968__optstandardscaler__use_scaler_flag                       | False                                                                                                                                                                                                                                                                                              |
| featureunion__float32_transform_139955258809968__float32_transform__activate_flag                         | True                                                                                                                                                                                                                                                                                               |
| numpypermutearray__axis                                                                                   | 0                                                                                                                                                                                                                                                                                                  |
| numpypermutearray__permutation_indices                                                                    | [1, 2, 3, 0, 4, 5]                                                                                                                                                                                                                                                                                 |
| lgbmclassifier__boosting_type                                                                             | gbdt                                                                                                                                                                                                                                                                                               |
| lgbmclassifier__class_weight                                                                              | balanced                                                                                                                                                                                                                                                                                           |
| lgbmclassifier__colsample_bytree                                                                          | 1.0                                                                                                                                                                                                                                                                                                |
| lgbmclassifier__importance_type                                                                           | split                                                                                                                                                                                                                                                                                              |
| lgbmclassifier__learning_rate                                                                             | 0.1                                                                                                                                                                                                                                                                                                |
| lgbmclassifier__max_depth                                                                                 | -1                                                                                                                                                                                                                                                                                                 |
| lgbmclassifier__min_child_samples                                                                         | 20                                                                                                                                                                                                                                                                                                 |
| lgbmclassifier__min_child_weight                                                                          | 0.001                                                                                                                                                                                                                                                                                              |
| lgbmclassifier__min_split_gain                                                                            | 0.0                                                                                                                                                                                                                                                                                                |
| lgbmclassifier__n_estimators                                                                              | 100                                                                                                                                                                                                                                                                                                |
| lgbmclassifier__n_jobs                                                                                    | 1                                                                                                                                                                                                                                                                                                  |
| lgbmclassifier__num_leaves                                                                                | 31                                                                                                                                                                                                                                                                                                 |
| lgbmclassifier__objective                                                                                 |                                                                                                                                                                                                                                                                                                    |
| lgbmclassifier__random_state                                                                              | 33                                                                                                                                                                                                                                                                                                 |
| lgbmclassifier__reg_alpha                                                                                 | 0.0                                                                                                                                                                                                                                                                                                |
| lgbmclassifier__reg_lambda                                                                                | 0.0                                                                                                                                                                                                                                                                                                |
| lgbmclassifier__silent                                                                                    | warn                                                                                                                                                                                                                                                                                               |
| lgbmclassifier__subsample                                                                                 | 1.0                                                                                                                                                                                                                                                                                                |
| lgbmclassifier__subsample_for_bin                                                                         | 200000                                                                                                                                                                                                                                                                                             |
| lgbmclassifier__subsample_freq                                                                            | 0                                                                                                                                                                                                                                                                                                  |

</details>

### Model Plot

<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` 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=[(&#x27;featureunion&#x27;,FeatureUnion(transformer_list=[(&#x27;float32_transform_139955258811312&#x27;,Pipeline(steps=[(&#x27;numpycolumnselector&#x27;,NumpyColumnSelector(columns=[1,2,3])),(&#x27;compressstrings&#x27;,CompressStrings(compress_type=&#x27;hash&#x27;,dtypes_list=[&#x27;char_str&#x27;,&#x27;char_str&#x27;,&#x27;char_str&#x27;],missing_values_reference_list=[&#x27;&#x27;,&#x27;-&#x27;,&#x27;?&#x27;,nan],misslist_list=[[],[],[]...NumpyReplaceMissingValues(missing_values=[])),(&#x27;numimputer&#x27;,NumImputer(missing_values=nan,strategy=&#x27;median&#x27;)),(&#x27;optstandardscaler&#x27;,OptStandardScaler(use_scaler_flag=False)),(&#x27;float32_transform&#x27;,float32_transform())]))])),(&#x27;numpypermutearray&#x27;,NumpyPermuteArray(axis=0,permutation_indices=[1, 2, 3, 0, 4, 5])),(&#x27;lgbmclassifier&#x27;,LGBMClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=1,random_state=33))])</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-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;featureunion&#x27;,FeatureUnion(transformer_list=[(&#x27;float32_transform_139955258811312&#x27;,Pipeline(steps=[(&#x27;numpycolumnselector&#x27;,NumpyColumnSelector(columns=[1,2,3])),(&#x27;compressstrings&#x27;,CompressStrings(compress_type=&#x27;hash&#x27;,dtypes_list=[&#x27;char_str&#x27;,&#x27;char_str&#x27;,&#x27;char_str&#x27;],missing_values_reference_list=[&#x27;&#x27;,&#x27;-&#x27;,&#x27;?&#x27;,nan],misslist_list=[[],[],[]...NumpyReplaceMissingValues(missing_values=[])),(&#x27;numimputer&#x27;,NumImputer(missing_values=nan,strategy=&#x27;median&#x27;)),(&#x27;optstandardscaler&#x27;,OptStandardScaler(use_scaler_flag=False)),(&#x27;float32_transform&#x27;,float32_transform())]))])),(&#x27;numpypermutearray&#x27;,NumpyPermuteArray(axis=0,permutation_indices=[1, 2, 3, 0, 4, 5])),(&#x27;lgbmclassifier&#x27;,LGBMClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=1,random_state=33))])</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-38" type="checkbox" ><label for="sk-estimator-id-38" class="sk-toggleable__label sk-toggleable__label-arrow">featureunion: FeatureUnion</label><div class="sk-toggleable__content"><pre>FeatureUnion(transformer_list=[(&#x27;float32_transform_139955258811312&#x27;,Pipeline(steps=[(&#x27;numpycolumnselector&#x27;,NumpyColumnSelector(columns=[1,2,3])),(&#x27;compressstrings&#x27;,CompressStrings(compress_type=&#x27;hash&#x27;,dtypes_list=[&#x27;char_str&#x27;,&#x27;char_str&#x27;,&#x27;char_str&#x27;],missing_values_reference_list=[&#x27;&#x27;,&#x27;-&#x27;,&#x27;?&#x27;,nan],misslist_list=[[],[],[]])),(&#x27;numpyreplacemissingvalues&#x27;...FloatStr2Float(dtypes_list=[&#x27;float_int_num&#x27;,&#x27;float_num&#x27;,&#x27;float_num&#x27;],missing_values_reference_list=[])),(&#x27;numpyreplacemissingvalues&#x27;,NumpyReplaceMissingValues(missing_values=[])),(&#x27;numimputer&#x27;,NumImputer(missing_values=nan,strategy=&#x27;median&#x27;)),(&#x27;optstandardscaler&#x27;,OptStandardScaler(use_scaler_flag=False)),(&#x27;float32_transform&#x27;,float32_transform())]))])</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"><label>float32_transform_139955258811312</label></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-39" type="checkbox" ><label for="sk-estimator-id-39" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyColumnSelector</label><div class="sk-toggleable__content"><pre>NumpyColumnSelector(columns=[1, 2, 3])</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-40" type="checkbox" ><label for="sk-estimator-id-40" class="sk-toggleable__label sk-toggleable__label-arrow">CompressStrings</label><div class="sk-toggleable__content"><pre>CompressStrings(compress_type=&#x27;hash&#x27;,dtypes_list=[&#x27;char_str&#x27;, &#x27;char_str&#x27;, &#x27;char_str&#x27;],missing_values_reference_list=[&#x27;&#x27;, &#x27;-&#x27;, &#x27;?&#x27;, nan],misslist_list=[[], [], []])</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-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyReplaceMissingValues</label><div class="sk-toggleable__content"><pre>NumpyReplaceMissingValues(missing_values=[])</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-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyReplaceUnknownValues</label><div class="sk-toggleable__content"><pre>NumpyReplaceUnknownValues(filling_values=nan,filling_values_list=[nan, nan, nan],known_values_list=[[170172835760119224333519554008280666130,140114708448418632577632402066430035116],[245397760256243238036686602120338271372,87378989482499796866217412016778320776,40061271003327253395033901872323469393],[245397760256243238036686602120338271372,40061271003327253395033901872323469393]],missing_values_reference_list=[&#x27;&#x27;, &#x27;-&#x27;, &#x27;?&#x27;, nan])</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-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label sk-toggleable__label-arrow">boolean2float</label><div class="sk-toggleable__content"><pre>boolean2float()</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-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label sk-toggleable__label-arrow">CatImputer</label><div class="sk-toggleable__content"><pre>CatImputer(missing_values=nan, strategy=&#x27;most_frequent&#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-45" type="checkbox" ><label for="sk-estimator-id-45" class="sk-toggleable__label sk-toggleable__label-arrow">CatEncoder</label><div class="sk-toggleable__content"><pre>CatEncoder(categories=&#x27;auto&#x27;, dtype=&lt;class &#x27;numpy.float64&#x27;&gt;, encoding=&#x27;ordinal&#x27;,handle_unknown=&#x27;error&#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-46" type="checkbox" ><label for="sk-estimator-id-46" class="sk-toggleable__label sk-toggleable__label-arrow">float32_transform</label><div class="sk-toggleable__content"><pre>float32_transform()</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"><label>float32_transform_139955258809968</label></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-47" type="checkbox" ><label for="sk-estimator-id-47" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyColumnSelector</label><div class="sk-toggleable__content"><pre>NumpyColumnSelector(columns=[0, 4, 5])</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-48" type="checkbox" ><label for="sk-estimator-id-48" class="sk-toggleable__label sk-toggleable__label-arrow">FloatStr2Float</label><div class="sk-toggleable__content"><pre>FloatStr2Float(dtypes_list=[&#x27;float_int_num&#x27;, &#x27;float_num&#x27;, &#x27;float_num&#x27;],missing_values_reference_list=[])</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-49" type="checkbox" ><label for="sk-estimator-id-49" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyReplaceMissingValues</label><div class="sk-toggleable__content"><pre>NumpyReplaceMissingValues(missing_values=[])</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-50" type="checkbox" ><label for="sk-estimator-id-50" class="sk-toggleable__label sk-toggleable__label-arrow">NumImputer</label><div class="sk-toggleable__content"><pre>NumImputer(missing_values=nan, 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-51" type="checkbox" ><label for="sk-estimator-id-51" class="sk-toggleable__label sk-toggleable__label-arrow">OptStandardScaler</label><div class="sk-toggleable__content"><pre>OptStandardScaler(use_scaler_flag=False)</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-52" type="checkbox" ><label for="sk-estimator-id-52" class="sk-toggleable__label sk-toggleable__label-arrow">float32_transform</label><div class="sk-toggleable__content"><pre>float32_transform()</pre></div></div></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-53" type="checkbox" ><label for="sk-estimator-id-53" class="sk-toggleable__label sk-toggleable__label-arrow">NumpyPermuteArray</label><div class="sk-toggleable__content"><pre>NumpyPermuteArray(axis=0, permutation_indices=[1, 2, 3, 0, 4, 5])</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-54" type="checkbox" ><label for="sk-estimator-id-54" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMClassifier</label><div class="sk-toggleable__content"><pre>LGBMClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=1, random_state=33)</pre></div></div></div></div></div></div></div>

## Evaluation Results

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# How to Get Started with the Model

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# Model Card Authors

This model card is written by following authors:

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# Model Card Contact

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# Citation

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**BibTeX:**
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# model_card_authors

wenpei

# model_description

test propose for autoai and hugging face