In kernel-based methods, i.e., as in our RF-RFA KRR model, inputs are nonlinearly transformed into a higher-dimensional space using the so-called kernel trick. In our KRR models, we focus on tuning two adjustable hyperparameters: the regularization coefficient and kernel width. For RF-RFA KRR models, we accelerated selection of these hyperparameters using the Bayesian optimization Python library Hyperopt 120 v0.