Fig. 2:

Effects of different sampling thresholds on prediction generalizability with SVM. With our sampling strategy, SVM performs very well on the training data at any threshold. We indicate the loss in training accuracy when the same model makes a prediction on a hold-out testing set to properly assess the effects of changing the sampling threshold and empirically determine the value for optimal results.