Building Machine Learning Systems with Python - Third Edition

Getting to know the Bayes theorem

At its core, the Naïve Bayes classification is nothing more than keeping track of which feature gives evidence to which class. The way the features are designed determines the model that is used to learn. The so-called Bernoulli model only cares about Boolean features; whether a word occurs only once or multiple times in a tweet does not matter. In contrast, the Multinomial model uses word counts as features. For the sake of simplicity, we will use the Bernoulli model to explain how to use Naïve Bayes for sentiment analysis. We will then use the Multinomial model later to set up and tune our real-world classifiers.

Let's assume the following meanings for the variables that we will use to explain Naïve Bayes: ...