Towards Aggregating Weighted Feature Attributions

Understand

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.