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.
The theory of cross-validation error for algorithms that predict boolean-valued attributes, presented in Section 2, is similar to the theory for real-valued attributes REF , but there are some differences.
A Theory of Cross-Validation Error
Journal of Experimental and Theoretical Artificial Intelligence, (1994), 6, 361-391
Mathematics Computer Science