Changelog

Bug fixes

• Fix a bug inclassification_report_imbalanced where the parametertarget_names was not taken into account whenoutput_dict=True.#989 byAYY7.

• SMOTENC now handles mix types of data type such as bool andpd.CategoricalDtype by delegating the conversion to scikit-learn encoder.#1002 byGuillaume Lemaitre.

• Handle sparse matrices inSMOTEN and raise a warning since it requires a conversion to dense matrices.#1003 byGuillaume Lemaitre.

• Remove spurious warning raised when minority class get over-sampled more than the number of sample in the majority class.#1007 byGuillaume Lemaitre.

Compatibility

• Maintenance release for being compatible with scikit-learn >= 1.3.0.#999 byGuillaume Lemaitre.

Deprecation

• The fitted attributeohe_ inSMOTENC is deprecated and will be removed in version 0.13. Usecategorical_encoder_ instead.#1000 byGuillaume Lemaitre.

• The default of the parameterssampling_strategy and replacement will change inBalancedRandomForestClassifier to follow the implementation of the original paper. This changes will take effect in version 0.13.#1006 byGuillaume Lemaitre.

Enhancements

• SMOTENC now accepts a parametercategorical_encoder allowing to specify aOneHotEncoder with custom parameters.#1000 byGuillaume Lemaitre.

• SMOTEN now accepts a parametercategorical_encoder allowing to specify aOrdinalEncoder with custom parameters. A new fitted parametercategorical_encoder_ is exposed to access the fitted encoder.#1001 byGuillaume Lemaitre.

• RandomUnderSampler andRandomOverSampler (whenshrinkage is notNone) now accept any data types and will not attempt any data conversion.#1004 byGuillaume Lemaitre.

• SMOTENC now support passing array-like ofstr when passing thecategorical_features parameter.#1008 by :userGuillaume Lemaitre <glemaitre>.

• SMOTENC now support automatic categorical inference whencategorical_features is set to"auto".#1009 by :userGuillaume Lemaitre <glemaitre>.