Exploring Ranked Local Selectors for Stable Explanations of ML Models

While complex machine learning methods can achieve great performance, human-interpretable details of their internal reasoning is to a large extent unavailable. Interpretable machine learning can remedy the lack of access to model reasoning but remains an elusive feat to fully achieve. Here we propose ranked selectors as a method for post-hoc explainability of classification outcomes from arbitrary classification models, with an initial emphasis on tabular data of moderate dimensions. The method is based on constructing a set of selectors, or rules, delimiting a local class consistent domain with maximal cover around the item of interest. The extended adjacent feature space is probed to achieve a ranking of the selectors. The method supports the use of an explicit foil class Q, allowing the formulation of contrastive queries in the form 'Why inference P instead of alternative inference Q?'. The answer is given as a short list of disjoint rules, a format previously shown to be amenable to human interpretation. We demonstrate the proposed method in open datasets, and elaborate on its stability aspects relative to other comparable methods.