A Theory of Cross-Validation Error
<|reference_start|>A Theory of Cross-Validation Error: 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.<|reference_end|>
@article{turney2002a,
    title={A Theory of Cross-Validation Error},
    author={Peter D. Turney (National Research Council of Canada)},
    journal={Journal of Experimental and Theoretical Artificial Intelligence,
  (1994), 6, 361-391},
    year={2002},
    number={NRC-35072},
    archivePrefix={arXiv},
    eprint={cs/0212029},
    primaryClass={cs.LG cs.CV}
}