In the previous sections you learned to use logistic regression for binary classification. In many classification problems, however, there are more than two classes that are of interest. We might wish to predict the genres of songs from samples of audio, or classify images of galaxies by their types. The goal of multi-class classification is to assign an instance to one of the set of classes. scikit-learn uses a strategy called one-vs.-all, or one-vs.-the-rest, to support multi-class classification. One-vs.-all
classification uses one binary classifier for each of the possible classes. The class that is predicted with the greatest confidence is assigned to the instance.
LogisticRegression supports multi-class classification ...