2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/31388This 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.48 pagesMachine LearningComputer Vision and Pattern RecognitionI.2.6; I.5.2text