Fig. 1

Assessment of the seven models in the training data (MEC). Models were trained using project pursuit (PPR), partial least squares (PLS), support vector machine with a linear kernel (SVM_lin), elastic net (GLMNET), random forests (RF), support vector machine with a radial basis function kernel (SVM_rad_sig), and gradient boosting machine (GBM). Model performances were assessed using mean absolute error (MAE), root mean squared error (RMSE), and R Squared. The boxplots summarizes these metrics across 100 cross validation datasets. Performances were similar across the models justifying use of an average of predictions in the ensemble model