Local Discrimination of Binary Classifiers via Localized AUC Functional

The paper introduces a new metric called Localized AUC (LAUC), a weighted version of the traditional AUC, to detect performance heterogeneity in risk prediction models. Using LAUC, the authors develop a method named LOCATOR that identifies patient cohorts where the model’s predictions are inaccurate. They validate LOCATOR on both real-world data and simulations and provide a statistical foundation for applying LAUC in this context.

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