Building and evaluating Naive Bayes classifier with WEKA

This is a followup post from previous where we were calculating Naive Bayes prediction on given data set. This time I want to demonstrate how all this can be implemented using WEKA application. For those who doesn’t know what WEKA is I highly recommend visiting their website and getting latest release. It is really powerful machine learning software written in Java. You can find plenty of tutorials in youtube on how to get started with WEKA. So I wont get in to details. I’m sure you’ll be able to follow anyway.

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Simple explanation of Naive Bayes classifier

Probably you’ve heard about Naive Bayes classifier and likely used in some GUI based classifiers like WEKA package. This is a number one algorithm used to see the initial results of classification. Sometimes surprisingly it outperforms the other models with speed, accuracy and simplicity. Lets see how this algorithm looks and what does it do. As you may know algorithm works on Bayes theorem of probability which allows to predict the class of unknown data set. Hoe you are comfortable with probability math – at least some basics.

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