The squared hinge loss is a loss function used for “maximum margin” binary classification problems. Mathematically it is defined as: where ŷ the predicted value and y is either 1 or -1. Thus, the squared hinge loss is: 0* when the true and predicted labels are the same and* when ŷ≥ 1 (which is an indication that the classifier is sure that it’s the correct label)quadratically increasing with the...