Kernel mean matching enhances risk estimation under spatial distribution shifts.

ABSTRACT: Accurate risk estimation under distribution shifts is critical for deploying machine learning models in real-world spatial applications, from ecological forecasting to medical image analysis. Conventional methods such as No Weighting (NW) and Importance Weighting (IW) fail in spatially structured data due to two challenges: (1) density ratio estimation in high-dimensional clustered distributions and (2) non-stationarity from environmental gradients or sampling biases. Classifier-based approaches offer partial improvements but often yield miscalibrated risk estimates by prioritizing discriminative accuracy over distribution alignment. We conduct a systematic evaluation of four risk estimation methods -NW, IW, Kernel Mean Matching (KMM), and classifier-based reweighting-across synthetic benc

SUBMITTER: Serov E 

PROVIDER: S-EPMC12917278 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature