From unbiased MDI Feature Importance to Explainable AI for Trees

• URL: http://arxiv.org/abs/2003.12043v4
• Date: Thu, 30 Sep 2021 14:35:53 GMT
• Title: From unbiased MDI Feature Importance to Explainable AI for Trees
• Authors: Markus Loecher
• Abstract summary: We show a common thread among the out-of-bag based bias correction methods and their connection to local explanation for trees. We also point out a bias caused by the inclusion of inbag data in the newly developed explainable AI for trees algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 explainable AI for trees algorithms.

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