Hinge loss — семейство функций потерь, используемых преимущественно в классификации на основе margin. Она штрафует прогнозы, находящиеся по неправильную сторону границы решения или слишком близко к ней, стимулируя больший разделяющий margin между классами. Машины опорных векторов часто используют hinge loss или родственные варианты.
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.