Hence, the portfolio models are to be compared with regard to validation error. Due to the growing flexibility, the training error is assumed to be the lowest for the model with the highest allowed complexity. Without requiring assumptions about the underlying true model, these can be compared quantitatively by a data set for validation to check for overfitting. With the purpose of also comparing extrapolation capabilities of the models, the validation data set is created by extracting the data points at extrema of the predictor variables. Finally, the model selection can be based on lowest validation error which also determines the required model complexity. The framework is illustrated in Figure .