Hinge Loss

Hinge loss is an important classification loss, especially in the context of support vector machines. It aims not only for correct classification, but also for separation with a sufficient margin between classes. In other words, it is not enough for the model to be correct; it is expected to be confidently separated from the wrong class. This is especially meaningful in problems where the decision boundary is expected to be clear and robust. In that sense, hinge loss considers both correctness and separation quality.