Naive Bayes is a probabilistic model that is, unsurprisingly, built upon a naive interpretation of Bayesian statistics. Despite the naive aspect, the method performs very well in a large number of contexts. Because of the naive aspect, it works quite quickly. It can be used for classification of many different feature types and formats, but we will focus on one in this chapter: binary features in the bag-of-words model.
For most of us, when we were taught statistics, we started from a frequentist approach. In this approach, we assume the data comes from some distribution and we aim to determine what the parameters are for that distribution. However, those parameters are (perhaps incorrectly) assumed to be fixed. We use our model to describe the data, even testing to ensure the data fits our model.
Bayesian statistics instead model how people (at least, non-frequentist statisticians) actually reason. We have some data, and we use that data to update our...