Table 3.

Accuracies of non-linear Net-Net classifiers.

ML Classifier	Test Accuracy	Test AUROC
Bayesian Nets	0.681	0.737
Naive Bayes Nets	0.586	0.636
Logistic Regression	0.618	0.668
Decision Table	0.516	0.552
MLP 1H	0.809	0.878
MLP 2H	0.827	0.902
Random Forest	0.832	0.914
Bagging REP	0.804	0.884
Bagging MLP	0.819	0.896
AdaBoostM1 MLP	0.821	0.884
Deep FC Nets	0.866	0.935

Note: please see Methods section for details on the classifier.