AI RESEARCH
Local MDI+: Local Feature Importances for Tree-Based Models
arXiv CS.LG
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ArXi:2506.08928v2 Announce Type: replace Tree-based ensembles such as random forests remain the go-to for tabular data over deep learning models due to their prediction performance and computational efficiency. These advantages have led to their widespread deployment in high-stakes domains, where interpretability is essential for ensuring trustworthy predictions. This has motivated the development of popular local feature importance methods such as LIME and Tree