Explaining Learning Performance with Local Performance Regions and Maximally Relevant Meta-Rules

Ricardo B. C. Prudêncio, Telmo de Menezes e Silva Filho

Abstract: Identifying instances in a learning task that are difficult to predict is important to avoid critical errors at deployment time. Additionally, providing explanations for good or bad predictions of a model can be useful to understand its behavior and to plan how to improve it (e.g., by data augmentation in specific areas of instances). In this paper, we propose a method to provide explanations for a model’s predictive performance based on the induction of meta-rules. Each meta-rule identifies a local region in the instance space, called Local Performance Region (LPR). The meta-rules are induced using a reduced number of attributes, in such a way that each LPR can be inspected by, e.g., plotting a pairwise attribute plot. Additionally, given a group of instances to explain (or eventually an individual instance), we propose a greedy-search algorithm that finds the subset of non-redundant LPRs that maximally covers the instances. By explaining the (in)correctness of model predictions, LPRs constitute a novel use of meta-learning and a novel application in explainable AI. Experiments show the usefulness of LPRs while explaining inaccurate class predictions of Random Forest in a benchmark dataset, demonstrating a special case of LPRs, called Local Hard Regions (LHRs).