Debiasing MDI Feature Importance and SHAP Values in Tree Ensembles

Markus Loecher

Abstract: We attempt to give a unifying view of the various recent attempts to (i) improve the interpretability of tree-based models and (ii) debias the default variable-importance measure in random forests, Gini importance. In particular, we demonstrate a common thread among the out-of-bag based bias correction methods and their connection to local explanation for trees. In addition, we point out a bias caused by the inclusion of inbag data in the newly developed SHAP values and suggest a remedy.