One Versus Rest (OvR) is a technique used to turn a binary classifier into a multi-class classifier. If the number of classes is N, then N binary classifiers are trained to distinguish each class versus the aggregate of the other classes. Given a sample to be classified, all N classifiers are called and the output is assigned based on the classifier that gives the highest probability (or highest confidence score) of the sample being of its corresponding class.