hinge loss

A family of loss functions for classification designed to find the decision boundary as distant as possible from each training example, thus maximizing the margin between examples and the boundary. KSVMs use hinge loss (or a related function, such as squared hinge loss). For binary classification, the hinge loss function is defined as follows:

where y is the true label, either -1 or +1, and y' is the raw output of the classification model:

Consequently, a plot of hinge loss versus (y * y') looks as follows:

Practitioners refer to hinge loss when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.