A theory of cross-validation error |
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| Authors: | PETER TURNEY |
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| Affiliation: | Knowledge Systems Laboratory, Institute for Information Technology, National Research Council Canada , Ottawa, Ontario, K1A 0R6, Canada Phone: 613-993-8564 E-mail: peter@ai.iit.nrc.ca |
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| Abstract: | 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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| Keywords: | cross-validation simplicity bias variance AIC linear regression instance-based learning |
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