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We introduce region-based explanations (RbX), a novel, model-agnostic methodto generate local explanations of scalar outputs from a black-box predictionmodel using only query access. RbX is based on a greedy algorithm for buildinga convex polytope that approximates a region of feature space where modelpredictions are close to the prediction at some target point. This region isfully specified by the user on the scale of the predictions, rather than on thescale of the features. The geometry of this polytope - specifically the changein each coordinate necessary to escape the polytope - quantifies the localsensitivity of the predictions to each of the features. These "escapedistances" can then be standardized to rank the features by local importance.RbX is guaranteed to satisfy a "sparsity axiom," which requires that featureswhich do not enter into the prediction model are assigned zero importance. Atthe same time, real data examples and synthetic experiments show how RbX canmore readily detect all locally relevant features than existing methods.

RbX: Region-based explanations of prediction models