Hinge loss is a family of loss functions used primarily in margin-based classification. It penalizes predictions that fall on the wrong side of a decision boundary or too close to the boundary, encouraging a larger separation margin between classes. Support vector machines commonly use hinge loss or related variants.
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: !A Cartesian plot consisting of two joined line segments. The first line segment starts at (-3, 4) and ends at (1, 0). The second line segment begins at (1, 0) and continues indefinitely with a slope of 0.