https://doi.org/10.1351/goldbook.10094
Cross validation of a data set of \(N\) objects in which \(N/G\) objects are removed at each iteration of the procedure.
Notes:
- Objects 1 to \(N/G\) are removed on the first iteration, then objects \(N/G + 1\) to \(2N/G\) after replacement of the first \(N/G\) objects, and so on.
- Because the perturbation of the model is larger than in leave-one-out cross validation, the prediction ability of the G-fold cross validation is less optimistic than obtained with leave-one-out cross validation.