This is a follow-up post from previous where we were calculating Naive Bayes prediction on the given data set. This time I want to demonstrate how all this can be implemented using the WEKA application. I highly recommend visiting their website and getting the latest release. WEKA is a compelling machine learning software written in Java. It is a widely-used and highly regarded machine learning software that offers a range of powerful data mining and modeling tools. It provides a user-friendly interface, making it accessible to both experienced and novice users. Weka offers a wide range of algorithms and data pre-processing techniques, making it a flexible and robust tool for various machine learning applications, such as classification, clustering, and association rule mining. You can find plenty of tutorials on youtube on how to get started with WEKA. So I won’t get into details. I’m sure you’ll be able to follow anyway.
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 prediction the class of unknown data sets. Hoe you are comfortable with probability math – at least some basics.