# From unbiased MDI Feature Importance to Explainable AI for Trees

arXiv: https://arxiv.org/abs/2003.12043 · HTML: https://mlsanity.com/paper/2003.12043/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Individualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of ℓ2 Regularization](https://mlsanity.com/paper/2211.04409/) (2022)
- [A survey and taxonomy of methods interpreting random forest models](https://mlsanity.com/paper/2407.12759/) (2024)
- [Data-driven advice for interpreting local and global model predictions in bioinformatics problems](https://mlsanity.com/paper/2108.06201/) (2021)

## Abstract (arXiv)

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.