A Theory of Cross-Validation Error

Details A Theory of Cross-Validation Error A Theory of Cross-Validation Error

2002-12-11

arxiv.org/abs/cs/0212029v1

Journal of Experimental and Theoretical Artificial Intelligence, (1994), 6, 361-391

Computer Science

Learning

48 pages

Scientific paper

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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