Abstract
In this work we link the understandability of machine learning models to the complexity of their SHapley Additive exPlanations (SHAP). Thanks to this reframing we introduce two novel metrics for understandability: SHAP Length and SHAP Interaction Length. These are model-agnostic, efficient, intuitive and theoretically grounded metrics that are anchored in
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