Which outputs the following: the function call, SVM type, kernel and cost (which is set to its default). In case you are wondering about gamma, although it's set to 0.5 here, it plays no role in linear SVMs. We'll say more about it in the sequel to this article in which we'll cover more complex kernels. More interesting are the support vectors. In a nutshell, these are training dataset points that specify the location of the decision boundary. We can develop a better understanding of their role by visualising them. To do this, we need to know their coordinates and indices (position within the dataset). This information is stored in the SVM model object.