Learning Data Mining with Python - Second Edition

Classifying with SVMs

After training the model, we have a line of maximum margin. The classification of new samples is then simply asking the question: does it fall above the line, or below it? If it falls above the line, it is predicted as one class. If it is below the line, it is predicted as the other class.

For multiple classes, we create multiple SVMs—each a binary classifier. We then connect them using any one of a variety of strategies. A basic strategy is to create a one-versus-all classifier for each class, where we train using two classes—the given class and all other samples. We do this for each class and run each classifier on a new sample, choosing the best match from each of these. This process is performed automatically in ...