For all the ML models derived in this study it was observed that hyperparameters played an important role in model validation accuracy. This is likely due to the relatively small size of the training set (595 datapoints). Thus a hyperparameter optimization algorithm was adopted in which hyperparameters were tuned with the help of Bayesian optimization based on Gaussian process regression (see table ). This algorithm searches through hyperparameter space by wrapping around noisy, expensive ML functions; after 50 calls (configuration attempts), the hyperparameter configuration returning the lowest validation error is saved together with the corresponding trained model. For all ML models (SVM, RF, MLR and DNN) convergence was observed from around 30 calls. MLR in this case does not have any hyperparameters to tune which means that in every SKOPT call the same model is trained which results in an equal validation error along calls.