Classification with Support Vector Machines
We first used SVMs for regression in Chapter 2, An Introduction to Regression. In this topic, you will find out how to use SVMs for classification. As always, we will use scikit-learn to run our examples in practice.
What Are Support Vector Machine Classifiers?
The goal of an SVM is to find a surface in an n-dimensional space that separates the data points in that space into multiple classes.
In two dimensions, this surface is often a straight line. However, in three dimensions, the SVM often finds a plane. These surfaces are optimal in the sense that they are based on the information available to the machine so that it can optimize the separation of the n-dimensional spaces.
The optimal separator found by the SVM is called the best separating hyperplane.
An SVM is used to find one surface that separates two sets of data points. In other words, SVMs are binary classifiers. This does not mean that SVMs can only be used for binary...
