For regression tasks, we generated partial least squares (PLS), random forest (RF), XGBoost (XGB) and multivariate linear regression (MLR) models, as well as support vector (SVR) and gaussian process (GPR) regressions using a radial basis function as kernel. For classification tasks, in addition to RF, XGB, support vector classification (SVC) and gaussian process classification (GPC) models, we tested the K-nearest neighbor (KNN) and logistic regression (LR) algorithms. For all classification models, fivefold cross-validation (CV) with 20 repeats was performed, with feature scaling and centering, as well as the Yeo-Johnson transformation applied as pre-processing steps. For hyperparameter tuning, the caret grid search with a tune length of 20 was applied (except for the case of XGB models where the length was reduced to 5). For all regression models both the number of CV repeats and the tune lengths were reduced to 10; for XGB models, the length was set to 3. The area under the receiver operating characteristic curve (AUC) was used as optimization metric for classification models and the root mean square error (RMSE) for regression tasks. Due to a large class imbalance at higher thresholds, down-sampling was performed for classification tasks, which improved model performance during preliminary test runs.