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New Features and Enhancements
The mlxtend.evaluate.feature_importance_permutation function has a new feature_groups argument to treat user-specified feature groups as single features, which is useful for one-hot encoded features. (#955)
The mlxtend.feature_selection.ExhaustiveFeatureSelector and SequentialFeatureSelector also gained support for feature_groups with a behavior similar to the one described above. (#957 and #965 via Nima Sarajpoor)
Changes
The custom_feature_names parameter was removed from the ExhaustiveFeatureSelector due to redundancy and to simplify the code base. The ExhaustiveFeatureSelector documentation illustrates how the same behavior and outcome can be achieved using pandas DataFrames. (#957)