Data Science Programming All-in-One For Dummies

Chapter 4

Learning with K-Nearest Neighbors

IN THIS CHAPTER

Understanding K-Nearest Neighbors in a basic way

Working with the right k parameter

Using KNN to perform regression

Using KNN to perform classification

Previous chapters in this minibook demonstrate that you have multiple options when it comes to performing regression and classification tasks. The K-Nearest Neighbors (KNN) algorithm is another way to perform these tasks (along with others) and it has its own sets of pros and cons. The chapter begins by introducing you to KNN and showing you some of the more basic tasks you can perform with it. Along the way, you discover how KNN differs from other methods of performing both regression and classification.

The next part of the chapter discusses tuning. The k parameter provides the means to tune your algorithm. The need for tuning is high with KNN because of the way it works. This part of the chapter helps you understand, through demonstration, why the k parameter is so important and how to choose one correctly.

The final sections of this chapter look at regression and classification. ...