Support Vector Machines

“Life is either a daring adventure or nothing. Security does not exist in nature, nor do the children of men as a whole experience it. Avoiding danger is no safer in the long run than exposure.”
– Helen Keller

So far, we have seen two parametric models: linear and logistic regression. We now encounter for the first time a non-parametric model.

TipChapter Objectives

In this chapter, we will learn the following:

  1. Kernel-based methods

  2. Support vector machines (SVM): concents and applications

  3. Implementation of support vector classifier (SVC) in scikit-learn

  4. Classifying popular datasets using SVC.