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 have used the latter as a classification model. In this section, we encounter for the first time a non-parametric model: support vector classifier. We delve into the concept of support vector machines and their applicability to solve classification problems.

But first, we revisit the problem of overfitting and mitigation techniques such as l_2 regularization in the context of binary logistic regression.

TipChapter Objectives

In this chapter, we will learn the following:

  1. Polynomial logistic regression

  2. l_2 regularization

  3. Hyperparameter tuning

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

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

  6. Classifying handwritten digits using SVC.