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.
The results obtained with gradient ascent are shown in Figures 14–18, whereas those for ADAM are presented in Figures 19–23
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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.