A Theory of Cross-Validation Error

Peter D. Turney

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A Theory of Cross-Validation Error

Peter D. Turney

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Abstract

This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore, the theory indicates precisely how these conflicting demands must be balanced, in order to minimize cross-validation error. A general theory is presented, then it is developed in detail for linear regression and instance-based learning.

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Peter D. Turney. 2002-12-11. A Theory of Cross-Validation Error. https://arxiv.org/abs/cs/0212029

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