We can also use SVMs to categorize multiple classes instead of just two. In this recipe, we will use a multi-class SVM to categorize the three types of flowers in the iris dataset.
By design, SVM algorithms are binary classifiers. However, there are a few strategies employed to get them to work on multiple classes. The two main strategies are called one versus all, and one versus one.
One versus one is a strategy where a binary classifier is created for each possible pair of classes. Then a prediction is made for a point for the class that has the most votes. This can be computationally hard as we must create classifiers for k classes.
Another way to implement multi-class classifiers is to do a one versus ...