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• W2892883002 abstract "Cross-validation (CV) is increasingly popular as a generic method to adjudicate between mathematical models of cognition and behavior. In order to measure model generalizability, CV quantifies out-of-sample predictive performance, and the CV preference goes to the model that predicted the out-of-sample data best. The advantages of CV include theoretic simplicity and practical feasibility. Despite its prominence, however, the limitations of CV are often underappreciated. Here, we demonstrate the limitations of a particular form of CV—Bayesian leave-one-out cross-validation or LOO—with three concrete examples. In each example, a data set of infinite size is perfectly in line with the predictions of a simple model (i.e., a general law or invariance). Nevertheless, LOO shows bounded and relatively modest support for the simple model. We conclude that CV is not a panacea for model selection." @default.
• W2892883002 created "2018-10-05" @default.
• W2892883002 creator A5063079579 @default.
• W2892883002 creator A5090294872 @default.
• W2892883002 date "2018-09-27" @default.
• W2892883002 modified "2023-10-16" @default.
• W2892883002 title "Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection" @default.
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• W2892883002 doi "https://doi.org/10.1007/s42113-018-0011-7" @default.
• W2892883002 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6400414" @default.
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• W2892883002 hasPublicationYear "2018" @default.
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