A critical aspect in evaluating the predictive performance of a model is the division of experimental data into a training set, used for model calibration, and a validation set, used to assess the model's generalization ability and prediction uncertainty. In this study, all models underwent a 5-fold cross-validation (5CV) procedure. In 5CV, the dataset was randomly partitioned into five subsets, with the model trained and validated five times. In each iteration, one subset served as the validation set, while the remaining four subsets were used for training. This approach enables predictions to be generated for all data points, in contrast to the conventional single-split method, which randomly (or stratified) divides the data into separate training and test sets.