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2017

A Unified Approach to Interpreting Model Predictions

Lundberg, Scott, Lee, Su-In

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Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications.

However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability.
In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another.
To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations).

A Unified Approach to Interpreting Model Predictions · Around

Built on

“A value for n-person games”
Lloyd Shapley · 1953
Earlier work this paper cites.
“Monotonic solutions of cooperative games”
H Young · 1985
Earlier work this paper cites.
“Extremal principle solutions of games in characteristic function form: core, Chebychev and Shapley value generalizations”
A Charnes, B Golany, M Keane and J Rousseau · 1988
Earlier work this paper cites.
“Analysis of regression in game theory approach”
Stan Lipovetsky and Michael Conklin · 2001
Earlier work this paper cites.

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