hinge loss

Hinge loss adds an increased penalty to misclassifications that are off by a large amount, since the cost function increases linearly as the decision function output moves further away from the actual label.

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The hinge loss function uses the distance between observations and the hyperplane that separates classes in the SVM algorithm in order to quantify the magnitude of error.

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