Version 0.3#

This release includes a number of bug fixes and enhancements related to hypothesis testing with decision trees. Moreover, we have added an experimental multi-view decision tree / random forest, which considers multiple views of the data when building trees. The documentation page has also undergone an organizational overhaul making it easier for users to find examples related to specific use cases.


  • Fix Fixes a bug in consistency of train/test samples when random_state is not set in FeatureImportanceForestClassifier and FeatureImportanceForestRegressor, by Adam Li (#135)

  • Fix Fixes a bug where covariate indices were not shuffled by default when running FeatureImportanceForestClassifier and FeatureImportanceForestRegressor test methods, by Sambit Panda (#140)

  • Enhancement Add multi-view splitter for axis-aligned decision trees, by Adam Li (#129)

  • Enhancement Add stratified sampling option to FeatureImportance* via the stratify keyword argument, by Yuxin Bai (#143)

  • Fix Fixed usage of feature_importances_ property in HonestForestClassifier, by Adam Li (#156)

  • Fix Fixed HonestForestClassifier to allow decision-trees from sklearn, albeit with a limited API, by Adam Li (#158)

Code and Documentation Contributors#

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