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secord-model
wendell-yu-2024/secord-model
secord-model is a tabular classification model from wendell-yu-2024. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for sklearn.
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Public
Updated Jul 8, 2024
Repo size
477 KB
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.joblib477 KB · 87%
At a glance
- Task
- Tabular Classification
- Library
- sklearn
- Access
- Public
- Created
- Jul 8, 2024
- Updated
- Jul 8, 2024
- SHA
- 8cfb1f7d
Model card
From the Hugging Face model README
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 |
Model Plot
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('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>Evaluation Results
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model_card_authors
wenpei
model_description
test propose for autoai and hugging face
- Task
- Tabular Classification
- Library
- sklearn
- Created
- Jul 8, 2024
- Updated
- Jul 8, 2024