%A Peter D. Turney %J Journal of Experimental and Theoretical Artificial Intelligence %T A theory of cross-validation error %X 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. %D 1994 %P 361-391 %L cogprints1820 %V 6