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.
F MISSINGPROOFS Proposition 2.Letπbe a ranking induced by a probabilistic valueϕ ω(fx)such thatϕ ω π(1)(fx)≥ ϕω π(2)(fx)≥ · · · ≥ϕ ω π(N) (fx)
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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.