Differential Accumulated Local Effects is a tool for quick and precise global explanations.
Abstract
Accumulated Local Effect (ALE) is a technique for precisely estimating feature effects that circumvents the main weaknesses of earlier approaches like partial dependence plots. The approach for estimating ALE from the sparse samples of the training set, or the approximation of ALE, has two drawbacks. First, it struggles to scale when the input is highly dimensional, and second, when the training set is small, it is susceptible to out-of-distribution (OOD) sampling. We propose a novel ALE approximation in this paper,
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