A theory of cross-validation error

1994 Journal of experimental and theoretical artificial intelligence (Print)  
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
more » ... and instance-based learning. = y f X ( ) σz + = z z z 1 ... z n = A Theory of Cross-Validation Error
doi:10.1080/09528139408953794 fatcat:cgdef6giivcdjmyytljgqraocq