hinge loss

Hinge loss is a type of loss function used in machine learning, particularly in binary classification problems. It quantifies the inefficiency of incorrect predictions made by a model, penalizing them by a certain amount. It measures the discrepancy between the predicted scores for different classes and the true class labels, encouraging correct predictions and minimizing errors.

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