Predicting classes with SVM

The support vector machine (SVM) algorithm is a classifier that works by finding the maximum distance between classes in multiple dimensions of data—effectively the largest gap between classes—and uses the middle point of that gap as a boundary for classification. In this recipe, we'll look at using the SVM for peforming supervised class prediction and illustrating the boundary graphically.

Getting ready

We'll continue to use the built-in iris dataset and the e1071 package.

How to do it...

Predicting classes with SVM...