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arXiv · 1907.04669

Optimal Explanations of Linear Models

Dimitris Bertsimas·Arthur Delarue·Patrick Jaillet·Sebastien Martin
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Abstract

When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerability to adversarial attacks. However, attempts at interpreting models are often ad hoc and application-specific, and the concept of interpretability itself is not well-defined. We propose a general optimization framework to create explanations for linear models. Our methodology decomposes a linear model into a sequence of models of increasing complexity using coordinate updates on the coefficients. Computing this decomposition optimally is a difficult optimization problem for which we propose exact algorithms and scalable heuristics. By solving this problem, we can derive a parametrized family of interpretability metrics for linear models that generalizes typical proxies, and study the tradeoff between interpretability and predictive accuracy.

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Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, Sebastien Martin. 2019-07-08. Optimal Explanations of Linear Models. https://arxiv.org/abs/1907.04669

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