Fig. 10.

Prediction errors distribution for each case study. Prediction errors (box-plots of normalized root mean square error in log-scale) of the calibrated models with and without regularization are shown for each case study. These distributions were obtained by calibrating the models to multiple sets of calibration data (as explained in section “Ill-conditioning, cross-validation and overfitting”) and cross-validating them on multiple cross-validation data sets. Most cases show the trend that better prior knowledge results in smaller cross-validation errors, i.e. regularized models are more generalizable