There are two kinds of machine learning models: generative models and discriminative models. Let's examine the following list of classifiers: decision trees, neural networks, random forests, generalized boosted models, logistic regression, naive bayes, and Support Vector Machine (SVM). Most of these are classifiers and ensemble models. The odd one out here is Naive Bayes. It's the only generative model in the list. The others are examples of discriminative models.
The fundamental difference between generative and discriminative models lies in the underlying probability inference structure. In this chapter, we will study the key concepts of generative models like types and GANs, but before that, let's go through some of the key differences between generative and discriminative models.