Explanations for Monotonic Classifiers

TL;DR

This paper introduces new algorithms for explaining monotonic classifiers, providing polynomial-time and model-agnostic methods to generate formal explanations for these specialized models.

Contribution

It presents novel, efficient algorithms for explaining monotonic classifiers, addressing the scarcity of classifier-specific explanation methods.

Findings

Algorithms are polynomial in runtime complexity.

Model-agnostic explanation enumeration is practically efficient.

Provides formal explanations for black-box monotonic classifiers.

Abstract

In many classification tasks there is a requirement of monotonicity. Concretely, if all else remains constant, increasing (resp. decreasing) the value of one or more features must not decrease (resp. increase) the value of the prediction. Despite comprehensive efforts on learning monotonic classifiers, dedicated approaches for explaining monotonic classifiers are scarce and classifier-specific. This paper describes novel algorithms for the computation of one formal explanation of a (black-box) monotonic classifier. These novel algorithms are polynomial in the run time complexity of the classifier and the number of features. Furthermore, the paper presents a practically efficient model-agnostic algorithm for enumerating formal explanations.