We first used support vector machines for regression in Lesson 3, *Regression*. In this topic, you will find out how to use support vector machines for classification. As always, we will use scikit-learn to run our examples in practice.

The goal of a support vector machines defined on an n-dimensional vector space is to find a surface in that n-dimensional space that separates the data points in that space into multiple classes.

In two dimensions, this surface is often a straight line. In three dimensions, the support vector machines often finds a plane. In general, the support vector machines finds a hyperplane. These surfaces are optimal in the sense that, based on the information available to the machine, it optimizes the separation of the n-dimensional spaces.

The optimal separator found by the support vector machines is called the **best separating hyperplane**.

A support vector machines is used to find one...