Hinge loss
Loss function in machine learning / From Wikipedia, the free encyclopedia
Dear Wikiwand AI, let's keep it short by simply answering these key questions:
Can you list the top facts and stats about Hinge loss?
Summarize this article for a 10 year old
In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs).[1]
For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined as
Note that should be the "raw" output of the classifier's decision function, not the predicted class label. For instance, in linear SVMs, , where are the parameters of the hyperplane and is the input variable(s).
When t and y have the same sign (meaning y predicts the right class) and , the hinge loss . When they have opposite signs, increases linearly with y, and similarly if , even if it has the same sign (correct prediction, but not by enough margin).