Introduction

In the previous chapter, you were introduced to regression models and learned how to fit a linear regression model with single or multiple variables, as well as with a higher-degree polynomial.

Unlike regression models, which focus on learning how to predict continuous numerical values (which can have an infinite number of values), classification, which will be introduced in this chapter, is all about splitting data into separate groups, also called classes.

For instance, a model can be trained to analyze emails and predict whether they are spam or not. In this case, the data is categorized into two possible groups (or classes). This type of classification is also called binary classification, which we will see a few examples of in this chapter. However, if there are more than two groups (or classes), you will be working on a multi-class classification (you will come across some examples of this in Chapter 4, An Introduction to Decision Trees).

But what is a...