2001-10-13Z2011-03-11T08:54:48Zhttp://cogprints.org/id/eprint/1820This item is in the repository with the URL: http://cogprints.org/id/eprint/18202001-10-13ZA theory of cross-validation errorThis 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.Peter D. Turney