Figure 5.

Bootstrapped test-set ROC-AUC of all models trained to predict transient new neurological deficit (tnND), sorted by mean ROC-AUC. SVM is the top-performing model, and LR represents the logistic regression baseline model (both highlighted). ROC-AUC = area under Receiver Operating Characteristic curve, SVM = support vector machine, QDA = quadratic discriminant analysis, LDA = linear discriminant analysis, ET = Extremely Randomized Trees, RF = Random Forest, XGB = extreme gradient boosting, LR = logistic regression, MLP = multilayer perceptron, KNN = k-nearest neighbors, GAM = generalized additive model.