A Theory of Cross-Validation Error
Machine Learning
2007-05-23 v1 Computer Vision and Pattern Recognition
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.
Cite
@article{arxiv.cs/0212029,
title = {A Theory of Cross-Validation Error},
author = {Peter D. Turney},
journal= {arXiv preprint arXiv:cs/0212029},
year = {2007}
}
Comments
48 pages