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:

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