Inverse Classification for Comparison-based Interpretability in Machine Learning

Understand

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data).

• It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint.
• This principle is implemented using observation generation in the Growing Spheres algorithm.
• Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.