It is possible to use the internal subsets to stop the training phase on the best test set performance in terms of RMSE. In this mode, performance can be estimated in an exhaustive or in a fast way. Exhaustive n-fold cross-validation (CV) consists of a nested CV procedure. At each step, 1 subset is left out as evaluation set, and the remaining subsets are used to generate a network ensemble in an n-1 CV training. In this CV training, the selected network configuration is the one that gives the minimum RMSE on the stopping set. Next the predictions for the evaluation data are estimated as a simple average of the prediction values for each network in the training ensemble. The exhaustive CV procedure adds one level to the cross-validation and increases greatly the running time. In alternative, the fast evaluation skips one nested level by using the same subset for stopping and evaluating performance, for a quicker but likely less accurate performance estimation.