Robust Counterfactual Explanations for Random Forests. (arXiv:2205.14116v1 [cs.LG])

Counterfactual explanations describe how to modify a feature vector in order
to flip the outcome of a trained classifier. Several heuristic and optimal
methods have been proposed to generate these explanations. However, the
robustness of counterfactual explanations when the classifier is re-trained has
yet to be studied. Our goal is to obtain counterfactual explanations for random
forests that are robust to algorithmic uncertainty. We study the link between
the robustness of ensemble models and the robustness of base learners and …

arxiv random random forests