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arXiv · 1901.10040

Towards Aggregating Weighted Feature Attributions

Umang Bhatt·Pradeep Ravikumar·Jose M. F. Moura
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Abstract

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.

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Umang Bhatt, Pradeep Ravikumar, Jose M. F. Moura. 2019-01-20. Towards Aggregating Weighted Feature Attributions. https://arxiv.org/abs/1901.10040

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