TreeGrad-Ranker produces feature rankings for decision trees by optimizing a joint insertion-deletion objective with O(L)-time gradients derived from the multilinear extension, outperforming probabilistic values like Shapley on standard metrics.
Suppose there exist a featurei∈[N]and a constantC∈Rsuch thatf x(S∪i) = fx(S) +Cfor everyS⊆[N]\i
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TreeGrad-Ranker: Feature Ranking via $O(L)$-Time Gradients for Decision Trees
TreeGrad-Ranker produces feature rankings for decision trees by optimizing a joint insertion-deletion objective with O(L)-time gradients derived from the multilinear extension, outperforming probabilistic values like Shapley on standard metrics.