Who Wins Where? Conformal Model Comparison for Local Superiority

Title:Who Wins Where? Conformal Model Comparison for Local Superiority

Abstract:Standard model comparison is global, aggregating losses across the covariate space to declare a single winner. This can obscure heterogeneous performance, where different models are preferable in different regions. We introduce conformalized local model comparison, a split-sample framework for constructing calibrated local best-model maps. Given a model comparison score, such as the difference between two squared losses, the method uses three disjoint splits to fit competing models, estimate local centers and scales from out-of-sample scores, and conformally calibrate residual uncertainty. At a target point, the procedure declares a local winner only when a one-sided conformal bound excludes a tie, with the score's sign determining the favored model. We prove finite-sample marginal control for one-sided erroneous declarations on the realized future comparison score, establish pointwise consistency of the localized mean-score estimator away from tie boundaries, show that aggregate comparison can disagree sharply with the prevalence of local superiority, and derive a squared-loss bias--variance decomposition that clarifies how model structure affects local wins. Synthetic and real-data experiments show that the method recovers heterogeneous winner regions, abstains under uncertainty, and yields higher conditional gain than global selection.

Machine Learning (cs.LG)

arXiv:2607.29053 [cs.LG]

(or arXiv:2607.29053v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.29053 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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