# Identifying the Most Explainable Classifier

arXiv: https://arxiv.org/abs/1910.08595 · HTML: https://mlsanity.com/paper/1910.08595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning](https://mlsanity.com/paper/2301.09042/) (2023)

## Abstract (arXiv)

We introduce the notion of pointwise coverage to measure the explainability properties of machine learning classifiers. An explanation for a prediction is a definably simple region of the feature space sharing the same label as the prediction, and the coverage of an explanation measures its size or generalizability. With this notion of explanation, we investigate whether or not there is a natural characterization of the most explainable classifier. According with our intuitions, we prove that the binary linear classifier is uniquely the most explainable classifier up to negligible sets.