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Hinge loss is a loss function used for binary classification tasks. It measures the distance between the predicted value and the true label. The hinge loss function is defined as:

L = max(0, 1 – y * f(x))

where f(x) is the predicted value and y is the true label (-1 or 1). The hinge loss is used in support vector machines (SVMs) and is typically used to train a model to classify examples into one of two categories.

The hinge loss function is designed such that the loss is 0 when the predicted value and the true label are correctly aligned (i.e., when y * f(x) > 1). If y * f(x) < 1, then the loss is equal to the distance between the predicted value and the true label. This encourages the model to predict values that are more confident and farther away from the decision boundary.