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--- |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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model_format: pickle |
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model_file: pipeline_model_sklearn.joblib |
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widget: |
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- structuredData: |
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Age: |
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- 23 |
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- 47 |
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- 47 |
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BP: |
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- HIGH |
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- LOW |
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- LOW |
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Cholesterol: |
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- HIGH |
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- HIGH |
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- HIGH |
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K: |
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- 0.031258 |
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- 0.056468 |
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- 0.068944 |
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Na: |
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- 0.792535 |
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- 0.739309 |
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- 0.697269 |
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Sex: |
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- F |
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- M |
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- M |
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--- |
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# Model description |
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[More Information Needed] |
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## Intended uses & limitations |
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[More Information Needed] |
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## Training Procedure |
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[More Information Needed] |
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### Hyperparameters |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|-----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| memory | | |
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| 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))] | |
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| verbose | False | |
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| 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())]))]) | |
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| numpypermutearray | NumpyPermuteArray(axis=0, permutation_indices=[1, 2, 3, 0, 4, 5]) | |
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| lgbmclassifier | LGBMClassifier(class_weight='balanced', n_jobs=1, random_state=33) | |
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| featureunion__n_jobs | | |
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| 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())]))] | |
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| featureunion__transformer_weights | | |
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| featureunion__verbose | False | |
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| 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())]) | |
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| 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())]) | |
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| featureunion__float32_transform_139955258811312__memory | | |
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| 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())] | |
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| featureunion__float32_transform_139955258811312__verbose | False | |
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| featureunion__float32_transform_139955258811312__numpycolumnselector | NumpyColumnSelector(columns=[1, 2, 3]) | |
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| 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=[[], [], []]) | |
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| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues | NumpyReplaceMissingValues(missing_values=[]) | |
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| 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]) | |
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| featureunion__float32_transform_139955258811312__boolean2float | boolean2float() | |
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| featureunion__float32_transform_139955258811312__catimputer | CatImputer(missing_values=nan, strategy='most_frequent') | |
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| featureunion__float32_transform_139955258811312__catencoder | CatEncoder(categories='auto', dtype=<class 'numpy.float64'>, encoding='ordinal',<br /> handle_unknown='error') | |
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| featureunion__float32_transform_139955258811312__float32_transform | float32_transform() | |
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| featureunion__float32_transform_139955258811312__numpycolumnselector__columns | [1, 2, 3] | |
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| featureunion__float32_transform_139955258811312__compressstrings__activate_flag | True | |
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| featureunion__float32_transform_139955258811312__compressstrings__compress_type | hash | |
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| featureunion__float32_transform_139955258811312__compressstrings__dtypes_list | ['char_str', 'char_str', 'char_str'] | |
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| featureunion__float32_transform_139955258811312__compressstrings__missing_values_reference_list | ['', '-', '?', nan] | |
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| featureunion__float32_transform_139955258811312__compressstrings__misslist_list | [[], [], []] | |
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| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues__filling_values | nan | |
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| featureunion__float32_transform_139955258811312__numpyreplacemissingvalues__missing_values | [] | |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__filling_values | nan | |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__filling_values_list | [nan, nan, nan] | |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__known_values_list | [[170172835760119224333519554008280666130, 140114708448418632577632402066430035116], [245397760256243238036686602120338271372, 87378989482499796866217412016778320776, 40061271003327253395033901872323469393], [245397760256243238036686602120338271372, 40061271003327253395033901872323469393]] | |
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| featureunion__float32_transform_139955258811312__numpyreplaceunknownvalues__missing_values_reference_list | ['', '-', '?', nan] | |
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| featureunion__float32_transform_139955258811312__boolean2float__activate_flag | True | |
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| featureunion__float32_transform_139955258811312__catimputer__activate_flag | True | |
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| featureunion__float32_transform_139955258811312__catimputer__missing_values | nan | |
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| featureunion__float32_transform_139955258811312__catimputer__sklearn_version_family | 1 | |
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| featureunion__float32_transform_139955258811312__catimputer__strategy | most_frequent | |
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| featureunion__float32_transform_139955258811312__catencoder__activate_flag | True | |
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| featureunion__float32_transform_139955258811312__catencoder__categories | auto | |
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| featureunion__float32_transform_139955258811312__catencoder__dtype | <class 'numpy.float64'> | |
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| featureunion__float32_transform_139955258811312__catencoder__encoding | ordinal | |
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| featureunion__float32_transform_139955258811312__catencoder__handle_unknown | error | |
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| featureunion__float32_transform_139955258811312__catencoder__sklearn_version_family | 1 | |
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| featureunion__float32_transform_139955258811312__float32_transform__activate_flag | True | |
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| featureunion__float32_transform_139955258809968__memory | | |
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| 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())] | |
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| featureunion__float32_transform_139955258809968__verbose | False | |
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| featureunion__float32_transform_139955258809968__numpycolumnselector | NumpyColumnSelector(columns=[0, 4, 5]) | |
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| featureunion__float32_transform_139955258809968__floatstr2float | FloatStr2Float(dtypes_list=['float_int_num', 'float_num', 'float_num'],<br /> missing_values_reference_list=[]) | |
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| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues | NumpyReplaceMissingValues(missing_values=[]) | |
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| featureunion__float32_transform_139955258809968__numimputer | NumImputer(missing_values=nan, strategy='median') | |
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| featureunion__float32_transform_139955258809968__optstandardscaler | OptStandardScaler(use_scaler_flag=False) | |
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| featureunion__float32_transform_139955258809968__float32_transform | float32_transform() | |
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| featureunion__float32_transform_139955258809968__numpycolumnselector__columns | [0, 4, 5] | |
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| featureunion__float32_transform_139955258809968__floatstr2float__activate_flag | True | |
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| featureunion__float32_transform_139955258809968__floatstr2float__dtypes_list | ['float_int_num', 'float_num', 'float_num'] | |
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| featureunion__float32_transform_139955258809968__floatstr2float__missing_values_reference_list | [] | |
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| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues__filling_values | nan | |
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| featureunion__float32_transform_139955258809968__numpyreplacemissingvalues__missing_values | [] | |
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| featureunion__float32_transform_139955258809968__numimputer__activate_flag | True | |
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| featureunion__float32_transform_139955258809968__numimputer__missing_values | nan | |
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| featureunion__float32_transform_139955258809968__numimputer__strategy | median | |
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| featureunion__float32_transform_139955258809968__optstandardscaler__use_scaler_flag | False | |
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| featureunion__float32_transform_139955258809968__float32_transform__activate_flag | True | |
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| numpypermutearray__axis | 0 | |
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| numpypermutearray__permutation_indices | [1, 2, 3, 0, 4, 5] | |
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| lgbmclassifier__boosting_type | gbdt | |
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| lgbmclassifier__class_weight | balanced | |
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| lgbmclassifier__colsample_bytree | 1.0 | |
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| lgbmclassifier__importance_type | split | |
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| lgbmclassifier__learning_rate | 0.1 | |
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| lgbmclassifier__max_depth | -1 | |
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| lgbmclassifier__min_child_samples | 20 | |
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| lgbmclassifier__min_child_weight | 0.001 | |
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| lgbmclassifier__min_split_gain | 0.0 | |
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| lgbmclassifier__n_estimators | 100 | |
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| lgbmclassifier__n_jobs | 1 | |
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| lgbmclassifier__num_leaves | 31 | |
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| lgbmclassifier__objective | | |
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| lgbmclassifier__random_state | 33 | |
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| lgbmclassifier__reg_alpha | 0.0 | |
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| lgbmclassifier__reg_lambda | 0.0 | |
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| lgbmclassifier__silent | warn | |
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| lgbmclassifier__subsample | 1.0 | |
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| lgbmclassifier__subsample_for_bin | 200000 | |
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| lgbmclassifier__subsample_freq | 0 | |
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</details> |
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### Model Plot |
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<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=[('featureunion',FeatureUnion(transformer_list=[('float32_transform_139955258811312',Pipeline(steps=[('numpycolumnselector',NumpyColumnSelector(columns=[1,2,3])),('compressstrings',CompressStrings(compress_type='hash',dtypes_list=['char_str','char_str','char_str'],missing_values_reference_list=['','-','?',nan],misslist_list=[[],[],[]...NumpyReplaceMissingValues(missing_values=[])),('numimputer',NumImputer(missing_values=nan,strategy='median')),('optstandardscaler',OptStandardScaler(use_scaler_flag=False)),('float32_transform',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))])</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=[('featureunion',FeatureUnion(transformer_list=[('float32_transform_139955258811312',Pipeline(steps=[('numpycolumnselector',NumpyColumnSelector(columns=[1,2,3])),('compressstrings',CompressStrings(compress_type='hash',dtypes_list=['char_str','char_str','char_str'],missing_values_reference_list=['','-','?',nan],misslist_list=[[],[],[]...NumpyReplaceMissingValues(missing_values=[])),('numimputer',NumImputer(missing_values=nan,strategy='median')),('optstandardscaler',OptStandardScaler(use_scaler_flag=False)),('float32_transform',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))])</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=[('float32_transform_139955258811312',Pipeline(steps=[('numpycolumnselector',NumpyColumnSelector(columns=[1,2,3])),('compressstrings',CompressStrings(compress_type='hash',dtypes_list=['char_str','char_str','char_str'],missing_values_reference_list=['','-','?',nan],misslist_list=[[],[],[]])),('numpyreplacemissingvalues'...FloatStr2Float(dtypes_list=['float_int_num','float_num','float_num'],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())]))])</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='hash',dtypes_list=['char_str', 'char_str', 'char_str'],missing_values_reference_list=['', '-', '?', 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=['', '-', '?', 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='most_frequent')</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='auto', dtype=<class 'numpy.float64'>, encoding='ordinal',handle_unknown='error')</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=['float_int_num', 'float_num', 'float_num'],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='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-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='balanced', n_jobs=1, random_state=33)</pre></div></div></div></div></div></div></div> |
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## Evaluation Results |
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[More Information Needed] |
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# How to Get Started with the Model |
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# Model Card Authors |
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This model card is written by following authors: |
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# Model Card Contact |
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You can contact the model card authors through following channels: |
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# Citation |
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Below you can find information related to citation. |
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**BibTeX:** |
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# model_card_authors |
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wenpei |
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# model_description |
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test propose for autoai and hugging face |
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