## Chapter 8. Kernel Models and Support Vector Machines

This chapter introduces kernel functions, binary support vectors classifiers, one-class support vector machines for anomaly detection, and support vector regression.

In the *Binomial classification* section in Chapter 6, *Regression and Regularization*, you learned the concept of hyperplanes to segregate observations from the training set and estimate the linear decision boundary. The logistic regression has at least one limitation: it requires that the datasets be linearly separated using a defined function (sigmoid). This limitation is especially an issue for high-dimension problems (large number of features that are highly nonlinearly dependent). **Support vector machines** (**SVMs**) overcome this limitation by estimating the optimal separating hyperplane using kernel functions.

In this chapter, we will cover the following topics:

The impact of some of the SVM configuration parameters and the kernel method on the accuracy of the classification

How...