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.
Source:
PAC, 2016, 88, 407. 'Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)' on page 420 (https://doi.org/10.1515/pac-2015-0605)