This chapter covered a lot of ground on two important topics. Firstly, we covered a popular probabilistic algorithm, Naive Bayes, and explained its concepts and showed how it uses bayes rule and conditional probability to make predictions about new data using a pre-trained model. We also mentioned why Naive Bayes is called Naive as it makes a Naive assumption that all its features are completely independent of each other, thereby occurrence of one feature does not impact the other in any way. Despite this it forms well as we saw in our sample application. In our sample application we learnt a technique called sentimental analysis for figuring out the opinion whether positive or negative from a piece of text.
In the next chapter, we will study another popular machine learning algorithm called decision tree. We will show how it is very similar to a flowchart and we will explain it using a sample loan approval application.