Supervised Feature Compression based on Counterfactual Analysis

TL;DR

This paper introduces a method that uses counterfactual explanations to identify decision boundaries, enabling the creation of an interpretable and compact decision tree that mimics a black-box classifier.

Contribution

It presents a novel supervised feature discretization technique based on counterfactual analysis to improve interpretability of black-box models.

Findings

Effective in maintaining accuracy while increasing sparsity

Produces interpretable decision trees closely resembling black-box models

Demonstrated on real-world datasets with positive results

Abstract

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.