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.
We remark that there are onlyO(D)polynomials that are needed on the spot and thus the complexity isO(D 2+L)
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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.