  A feature-based model explanation denotes how much each input feature
contributes to a model's output for a given data point. As the number of
proposed explanation functions grows, we lack quantitative evaluation criteria
to help practitioners know when to use which explanation function. This paper
proposes quantitative evaluation criteria for feature-based explanations: low
sensitivity, high faithfulness, and low complexity. We devise a framework for
aggregating explanation functions. We develop a procedure for learning an
aggregate explanation function with lower complexity and then derive a new
aggregate Shapley value explanation function that minimizes sensitivity.
