  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.
