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Abstract Data splitting is the act of partitioning available data into two portions, usually for cross-validatory purposes. One portion of the data is used to develop a predictive model and the other to evaluate the model's performance. This article reviews data splitting in the context of regression. Guidelines for splitting are described, and the merits of predictive assessments derived from data splitting relative to those derived from alternative approaches are discussed.
Picard et al. (Tue,) studied this question.