All models performed much better than a naïve classifier (50% accuracy on the balanced test set), with the RF model achieving the highest accuracy on the testing set (Figure ). Additional classification performance metrics for the testing dataset (F1-score, precision, recall, AUC, in Figure ) show uniformly excellent performance for most of the models. The marginally worse performance of the LR models suggests that a non-linear decision boundary exists with the selected featurization; nevertheless, the excellent overall accuracy of the other models illustrates that the structurestability relationship is surprisingly easy to learn.