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...