  Current approaches for explaining machine learning models fall into two
distinct classes: antecedent event influence and value attribution. The former
leverages training instances to describe how much influence a training point
exerts on a test point, while the latter attempts to attribute value to the
features most pertinent to a given prediction. In this work, we discuss an
algorithm, AVA: Aggregate Valuation of Antecedents, that fuses these two
explanation classes to form a new approach to feature attribution that not only
retrieves local explanations but also captures global patterns learned by a
model. Our experimentation convincingly favors weighting and aggregating
feature attributions via AVA.
