The Naive Bayes algorithm is one of the most popular machine learning algorithms, and for good reason: it is fast, easy to implement, and often makes good predictions even from noisy data. It’s the algorithm typically used by spam filters, and often used to build a baseline model for evaluating the performance of other algorithms (sort of a null hypothesis, if you will).
On the following page, you’ll find a step-by-step explanation of how the Naive Bayes algorithm works. As with most machine learning algorithms, there is a lot of computation involved, so I’ve used the R programming language to do the heavy lifting. You can find the explanation here: