For example, if we have three classes, y ∈{1,2,3}, we create copies of the original datasets and modify them.

• In the first copy, we replace all labels not equal to 1 by 0. In the second copy, we replace all labels not equal to 2 by 0. In the third copy, we replace all labels not equal to 3 by 0. Now we have three binary classification problems where we have to learn to distinguish between labels 1 and 0, 2 and 0, and 3 and 0.
• Once we have the three models, to classify the new input feature vector 𝐱, we apply the three models to the input, and we get three predictions.
• We then pick the prediction of a non-zero class which is the most certain. Remember that in logistic regression, the model returns not a label but a score (between 0 and 1) that can be interpreted as the probability that the label is positive. We can also interpret this score as the certainty of prediction.