So far, we have learned about the discriminative model, which learns to discriminate between the classes. That is, given an input, it tells us which class they belong to. For instance, to predict whether an email is a spam or ham, the model learns the decision boundary that best separates the two classes (spam and ham), and when a new email comes in they can tell us which class the new email belongs to.
In this chapter, we will learn about a generative model that learns the class distribution, that is, the characteristics of the classes rather than learning the decision boundary. As the name suggests, with the generative models, we can generate new data points similar to the data points present in the training set.
We will start off the chapter by understanding the difference between the discriminative and generative models in detail. Then, we will...
