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).
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Recommended Reading
Efron B (1982) The Jackknife, the bootstrap and other resampling plans. In: CBMS-NSF regional conference series in applied mathematics 1982. Society for Industrial and Applied Mathematics (SIAM), Philadelphia
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(2017). Leave-One-Out Cross-Validation. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_469
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_469
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