Definition
Leave-one-out cross-validation is a special case of cross-validation where the number of folds equals the number of instances in the data set. Thus, the learning algorithm is applied once for each instance, using all other instances as a training set and using the selected instance as a single-item test set. This process is closely related to the statistical method of jack-knife estimation (Efron, 1982).
Cross References
This is a preview of subscription content, log in via an institution to check access.
Recommended Reading
Efron, B. (1982). The Jackknife, the Bootstrap and other resampling plans. In CBMS-NSF regional conference series in applied mathematics 1982. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
(2011). Leave-One-Out Cross-Validation. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_469
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_469
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering