Classifying data with a linear SVM

In the first chapter, we saw some examples of classification with SVMs. We focused on SVMs' slightly superior classification performance compared to logistic regression, but for the most part, we left SVMs alone.

Here, we will focus on them more closely. While SVMs do not have an easy probabilistic interpretation, they do have an easy visual-geometric one. The main idea behind linear SVMs is to separate two classes with the best possible plane.

Let's linearly separate two classes with an SVM.

Getting ready

Let us start by loading and visualizing the iris dataset available in scikit-learn:

Load the data

Load part of the iris dataset. This will allow for easy comparison with the first chapter:

#load the libraries we have been using
import numpy as np      
import pandas as pd     
import matplotlib.pyplot as plt  #Library for visualization

from sklearn import datasets

iris = datasets.load_iris()
X_w = iris.data[:, :2]  #load the first two features of the iris...