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).
- 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), PhiladelphiaGoogle Scholar