  Loss functions drive the optimization of machine learning algorithms. The
choice of a loss function can have a significant impact on the training of a
model, and how the model learns the data. Binary classification is one of the
major pillars of machine learning problems, used in medical imaging to failure
detection applications. The most commonly used surrogate loss functions for
binary classification include the binary cross-entropy and the hinge loss
functions, which form the focus of our study.
  In this paper, we provide an overview of a novel loss function, the Xtreme
Margin loss function. Unlike the binary cross-entropy and the hinge loss
functions, this loss function provides researchers and practitioners
flexibility with their training process, from maximizing precision and AUC
score to maximizing conditional accuracy for a particular class, through
tunable hyperparameters $\lambda_1$ and $\lambda_2$, i.e., changing their
values will alter the training of a model.
