Machine Learning with Python Cookbook, 2nd Edition

Chapter 17. Support Vector Machines

17.0 Introduction

To understand support vector machines, we must understand hyperplanes. Formally, a hyperplane is an n – 1 subspace in an n-dimensional space. While that sounds complex, it actually is pretty simple. For example, if we wanted to divide a two-dimensional space, we’d use a one-dimensional hyperplane (i.e., a line). If we wanted to divide a three-dimensional space, we’d use a two-dimensional hyperplane (i.e., a flat piece of paper or a bed sheet). A hyperplane is simply a generalization of that concept into n dimensions.

Support vector machines classify data by finding the hyperplane that maximizes the margin between the classes in the training data. In a two-dimensional example with two classes, we can think of a hyperplane as the widest straight “band” (i.e., line with margins) that separates the two classes.

In this chapter, we cover training support vector machines in a variety of situations and dive under the hood to look at how we can extend the approach to tackle common problems.

17.1 Training a Linear Classifier

Problem

You need to train a model to classify observations.

Solution

Use a support vector classifier (SVC) to find the hyperplane that maximizes the margins between the classes:

# Load libraries
from sklearn.svm import LinearSVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np

# Load data with only two classes and two features
iris = datasets.load_iris()
features ...