Develops a method to find minimal input perturbations that flip GBDT predictions by extending random-forest counterfactuals to account for sequential tree dependencies and negative-gradient training.
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Explaining Predictions from Tree-based Boosting Ensembles
Develops a method to find minimal input perturbations that flip GBDT predictions by extending random-forest counterfactuals to account for sequential tree dependencies and negative-gradient training.