FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles

Model interpretability has become an important problem in machine learning (ML) due to the increased effect algorithmic decisions have on humans. Counterfactual explanations can help users understand not only why ML models make certain decisions, but also how these be changed. We frame of finding counterfactual as optimization task and extend previous work that could applied differentiable models. In order accommodate non-differentiable such tree ensembles, we use probabilistic model approximations framework. introduce approximation technique is effective for predictions original show our examples are significantly closer instances than those produced by other methods specifically designed ensembles.