Explanations for Monotonic Classifiers
This addresses the need for interpretable explanations in monotonic classification tasks, which is crucial for domains like finance or healthcare where model decisions must be transparent and adhere to monotonic constraints.
The paper tackles the problem of explaining monotonic classifiers, where predictions must not decrease with increasing feature values, by developing novel algorithms for computing formal explanations that are polynomial in classifier runtime and feature count, and presents a model-agnostic algorithm for enumerating these explanations.
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.