Chapter 5. Naïve Bayes Classifiers
This chapter introduces the most common and simple generative classifiers—Naïve Bayes. As a reminder, generative classifiers are supervised learning algorithms that attempt to fit a joint probability distribution, p(X,Y), of two events X and Y, representing two sets of observed and hidden (or latent) variables, x and y.
In this chapter, you will learn, and hopefully appreciate, the simplicity of the Naïve Bayes technique through a concrete example. Then, you will build a Naïve Bayes classifier to predict stock price movement, given some prior technical indicators in the analysis of financial markets.
Finally, you will apply Naïve Bayes to text mining by predicting stock prices, using financial news feed and press releases.
