Version 0.4#

This version patches some issues with the FeatureImportance* classes and also adds a feature to the MultiViewDecisionTreeClassifier class that allows one to scale the number of split candidates sampled per feature-set equally.


  • API Change FeatureImportanceForest* now has a hyperparameter to control the number of permutations is done per forest permute_per_forest_fraction, by Adam Li (#145)

  • Enhancement Add dataset generators for regression and classification and hypothesis testing, by Adam Li (#169)

  • Fix Fixes a bug where FeatureImportanceForest* was unable to be run when calling statistic with covariate_index defined for MI, AUC metrics, by Adam Li (#164)

  • Enhancement Add sktree.experimental.conditional_resample(), which allows conditional resampling of rows based on nearest-neighbors defined via a feature set, by Adam Li (#170)

  • Enhancement Multi-view trees now are able to scale the sampling of split candidates at the same rate per feature-set now, which means ‘sqrt’ would sample split candidates equal to the square root of each feature-set size, by Adam Li (#152)

  • Fix Fixes bug in sktree.tree.MultiViewDecisionTreeClassifier where the max_features argument applied over

    more than two views with apply_max_features_per_set set to True results in an incorrect and oversampled number of max_features in the views after the first two, by Adam Li (#172)

Code and Documentation Contributors#

Thanks to everyone who has contributed to the maintenance and improvement of the project since version inception, including: