Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, an optimal Decision Tree can be trained that resembles the black-box model, but that is interpretable and compact. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity.

翻译：反事实解释正在成为事后可解释机器学习中的事实标准。对于给定分类器及被归类至非期望类别的实例，其反事实解释对应于能使分类结果发生改变的该实例的微小扰动。本研究旨在利用反事实解释检测预训练黑箱模型的关键决策边界，并据此构建具备可调粒度的数据集特征的有监督离散化方案。通过离散化后的数据集，可训练出与黑箱模型近似但具备可解释性与紧凑性的最优决策树。对真实数据集的数值结果表明，该方法在准确性与稀疏性方面具有有效性。