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...