Fig 1. In the “Cross-validation and testing” approach, the data are divided into two separate sets (cross-validation set and test set) only once.

First, different models are trained and validated with cross-validation and the best set of parameters is chosen. Prediction accuracy and statistical significance of the parameters are evaluated on the test set, after training on the cross-validation set.