Table 2.

Summary statistics of cross-validated ROC AUC for all models fit.

Model	Cross-validated ROC AUC
	M	Median	SD	Min	Max
Neural network	0.696	0.714	0.107	0.519	0.811
Ridge regression	0.686	0.714	0.095	0.532	0.783
XGBoost	0.659	0.667	0.161	0.429	0.833
Linear with PCA	0.647	0.667	0.075	0.532	0.717
Logistic with PCA	0.641	0.652	0.082	0.506	0.714
SVM	0.625	0.636	0.104	0.481	0.727
Regression tree	0.621	0.667	0.064	0.533	0.669
Elastic net	0.597	0.500	0.132	0.500	0.750
AdaBoost	0.582	0.636	0.205	0.234	0.740
GBM	0.581	0.584	0.140	0.364	0.742
Random forest	0.581	0.583	0.188	0.286	0.758
LASSO	0.525	0.500	0.056	0.500	0.625

SVM: support vector machine; PCA: principal component analysis; GBM: generalized boosting model; LASSO: least absolute shrinkage and selection operator; ROC: receiver operating characteristic; AUC: area under the curve.