Cross-validation for geospatial data: Estimating generalization performance in geostatistical problems

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  • Jing Wang
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
  • Laurel Hopkins
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
  • Tyler Hallman
  • W Douglas Robinson
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
  • Rebecca Hutchinson
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
Geostatistical learning problems are frequently characterized by spatial autocorrelation in the input features and/or the potential for covariate shift at test time. These realities violate the classical assumption of independent, identically distributed data, upon which most cross-validation algorithms rely in order to estimate the generalization performance of a model. In this paper, we present a theoretical criterion for unbiased cross-validation estimators in the geospatial setting. We also introduce a new cross-validation algorithm to
evaluate models, inspired by the challenges of geospatial problems. We apply a framework for categorizing problems into different types of geospatial scenarios to help practitioners select an appropriate cross-validation strategy. Our empirical analyses compare cross-validation algorithms on both simulated and several real datasets to develop recommendations for a variety of geospatial settings. This paper aims to draw attention to some challenges that arise in model evaluation for geospatial problems and to provide guidance for users.
Iaith wreiddiolSaesneg
CyfnodolynTransactions on Machine Learning Research
StatwsCyhoeddwyd - 4 Hyd 2023

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