AI RESEARCH
WOODELF-HD: Efficient Background SHAP for High-Depth Decision Trees
arXiv CS.LG
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ArXi:2604.10569v1 Announce Type: new Decision-tree ensembles are a cornerstone of predictive modeling, and SHAP is a standard framework for interpreting their predictions. Among its variants, Background SHAP offers high accuracy by modeling missing features using a background dataset. Historically, this approach did not scale well, as the time complexity for explaining n instances using m background samples included an O(mn) component. Recent methods such as Woodelf and PLTreeSHAP reduce this to O(m+n), but.