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Shapley value is a popular approach for measuring the influence of individualfeatures. While Shapley feature attribution is built upon desiderata from gametheory, some of its constraints may be less natural in certain machine learningsettings, leading to unintuitive model interpretation. In particular, theShapley value uses the same weight for all marginal contributions -- i.e. itgives the same importance when a large number of other features are givenversus when a small number of other features are given. This property can beproblematic if larger feature sets are more or less informative than smallerfeature sets. Our work performs a rigorous analysis of the potentiallimitations of Shapley feature attribution. We identify simple settings wherethe Shapley value is mathematically suboptimal by assigning larger attributionsfor less influential features. Motivated by this observation, we proposeWeightedSHAP, which generalizes the Shapley value and learns which marginalcontributions to focus directly from data. On several real-world datasets, wedemonstrate that the influential features identified by WeightedSHAP are betterable to recapitulate the model's predictions compared to the featuresidentified by the Shapley value.

WeightedSHAP: analyzing and improving Shapley based feature attributions