  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.
