Machine Learning With Go

Overview of naive bayes and its big assumption

Naive bayes operates under one large assumption. This assumption says that the probability of the classes and the presence or absence of a certain feature in our dataset is independent of the presence or absence of other features in our dataset. This allows us to write a very simple formula for the probability of a certain class, given the presence or absence of certain features.

Let's take an example to make this more concrete. Again, let's say that we are trying to predict two classes of emails, A and B (spam and not spam, for example), based on words in the emails. Naive bayes would assume that the presence/absence of a certain word is independent of other words. If we make this assumption, ...