TreeGrad-Ranker: Feature Ranking via $O(L)$-Time Gradients for Decision Trees

ArXi:2602.11623v2 Announce Type: replace We revisit the use of probabilistic values, which include the well-known Shapley and Banzhaf values, to rank features for explaining the local predicted values of decision trees. The quality of feature rankings is typically assessed with the insertion and deletion metrics. Empirically, we observe that co-optimizing these two metrics is closely related to a joint optimization that selects a subset of features to maximize the local predicted value while minimizing it for the complement.