Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm

Last Updated on August 12, 2019

Naive Bayes is a simple and powerful technique that you should be testing and using on your classification problems.

It is simple to understand, gives good results and is fast to build a model and make predictions. For these reasons alone you should take a closer look at the algorithm.

In a recent blog post, you learned how to implement the Naive Bayes algorithm from scratch in python.

In this post you will learn tips and tricks to get the most from the Naive Bayes algorithm.

Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples.

1. Missing Data

Naive Bayes can handle missing data.

Attributes are handled separately by the algorithm at both model construction time and prediction time.

As such, if a data instance has a missing value for an attribute, it
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