Abstract

The Naïve Bayes series of algorithms are some of the simplest classification algorithms, but they tend to offer reasonably good results very quickly for a number of problems, including Natural Language Processing problems such as spam classification, as well as more classical feature-driven classification. In this talk, we will look at the math behind Naïve Bayes classification, solving problems by hand before looking at a package in R which solves the problem for us. By the end of this talk, you should be able to apply Naïve Bayes to existing problems. No experience with statistics is required, although there will be a small amount of math.


Slides

The slides are available as a GitPitch slide deck.

The slides are licensed under Creative Commons Attribution-ShareAlike.


Demo Code

The demonstration code is available on my GitHub repository. This includes a set of Jupyter notebooks which walk through our examples.

The source code is licensed under the terms offered by the GPL. The slides are licensed under Creative Commons Attribution-ShareAlike.


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