Summary
In this chapter, you learned about the fundamental concepts of machine learning classification, including types of classification, classification performance evaluation, cross-validation, and model tuning. You also learned about the simple, yet powerful, classifier, Naïve Bayes. We went in depth through the mechanics and implementations of Naïve Bayes with a couple of examples, the most important one being the movie recommendation project.
Binary classification using Naïve Bayes was the main talking point of this chapter. In the next chapter, we will solve ad click-through prediction using another binary classification algorithm: a decision tree.
