Shapley Based Residual Decomposition for Instance Analysis
Tommy Liu Amanda Barnard
In this paper, we introduce the idea of decomposing the residuals of
regression with respect to the data instances instead of features. This allows
us to determine the effects of each individual instance on the model and each
other, and in doing so makes for a model-agnostic method of identifying
instances of interest. In doing so, we can also determine the appropriateness
of the model and data in the wider context of a given study. The paper focuses
on the possible applications that such a framework brings to the relatively
unexplored field of instance analysis in the context of Explainable AI tasks.