In this chapter, we will start by using a support vector machine (SVM) with a linear kernel to get a rough idea of how SVMs work. They create a hyperplane, or linear surface in several dimensions, which best separates the data.
In two dimensions, this is easy to see: the hyperplane is a line that separates the data. We will see the array of coefficients and intercept of the SVM. Together they uniquely describe a scikit-learn linear SVC predictor.
In the rest of the chapter, the SVMs have a radial basis function (RBF) kernel. They are nonlinear, but with smooth separating surfaces. In practice, SVMs work well with many datasets and thus are an integral part of the scikit-learn library.
