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 train gradient boosted trees on the same datasets using GradientBoostingClassifier and GradientBoostingRegres- sor from the scikit-learn library
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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.