Papers › Efficient Shapley Performance Attribution for Least-Squares Regression
Efficient Shapley Performance Attribution for Least-Squares Regression
Logan Bell, Nikhil Devanathan, Stephen Boyd
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We consider the performance of a least-squares regression model, as judged by out-of-sample R². Shapley values give a fair attribution of the performance of a model to its input features, taking into account interdependencies between features. Evaluating the Shapley values exactly requires solving a number of regression problems that is exponential in the number of features, so a Monte Carlo-type approximation is typically used. We focus on the special case of least-squares regression models, where several tricks can be used to compute and evaluate regression models efficiently. These tricks give a substantial speed up, allowing many more Monte Carlo samples to be evaluated, achieving better accuracy. We refer to our method as least-squares Shapley performance attribution (LS-SPA), and describe our open-source implementation.
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