2023/01/22 by Brett Mullins, Mullins, Brett · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2301.09042
openalex publication_date 2023/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for arbitrary or black-box classifiers. But what makes these methods work in general? We introduce a topological framework for rule-based explanation methods and provide a characterization of explainability in terms of the definability of a classifier relative to an explanation scheme. We employ this framework to consider various explanation schemes and argue that the preferred scheme depends on how much the user knows about the domain and the probability measure over the feature space.