Schematic description of nested cross-validation.

The “inner” cross-validation: The “inner” cross-validation is for model selection based on their accuracy with unseen data. Here, the models are repeatedly fitted to different random subsets (training data), and their accuracy is evaluated with the data not used for fitting (test data). The model’s accuracy with the test data was averaged through iterations and used for model selection.