Naive Bayes

In previous chapters, we introduced two models for classification tasks: k-Nearest Neighbors (KNN) and logistic regression. In this chapter, we will introduce another family of classifiers called Naive Bayes. Named for its use of Bayes' theorem and for its naive assumption that all features are conditionally independent of each other given the response variable, Naive Bayes is the first generative model that we will discuss. First, we will introduce Bayes' theorem. Next, we will compare generative and discriminative models. We will discuss Naive Bayes and its assumptions and examine its common variants. Finally, we will fit a model using scikit-learn.