achieves_planned_objective statistical model validation K-fold cross validation method Tina Boussard Person:Helen Parkinson K-fold cross validation method K-fold cross-validation randomly partitions the original sample into K subsamples. Of the K subsamples, a single subsample is retained as the validation data for testing the model, and the remaining K - 1 subsamples are used as training data. The cross-validation process is then repeated K times (the folds), with each of the K subsamples used exactly once as the validation data. The K results from the folds then can be averaged (or otherwise combined) to produce a single estimation. The advantage of this method over repeated random sub-sampling is that all observations are used for both training and validation, and each observation is used for validation exactly once. 10-fold cross-validation is commonly used cross validation objective metadata incomplete