Abstract: Binary classification models can be trained to classify data as being in one of two classes. Membership in a class may be imbalanced so that there are more members in one class than the other. Additionally, one of the classes may have a higher importance than the other, yet appear much less frequently. It is possible to train the binary classification model using a base loss function and a regularization function based on a ranking of training results in order to reduce the false positives at a high true positive rate.