Implementing Naive Bayes' classification

Naive Bayes is a simple probabilistic classifier based on the Bayes theorem. This classifier is capable of calculating the most probable output depending on the input. It is possible to add new raw data at runtime and have a better probabilistic classifier. The Naive Bayes model is typically used for classification. There will be a bunch of features X1, X2,....Xn observed for an instance. The goal is to infer to which class among the limited set of classes the particular instance belongs. This model makes the assumption that every pair of features Xi and Xj is conditionally independent given the class. This classifier is a sub-class of Bayesian networks. For more information about the classifier, please refer to http://www.statsoft.com/textbook/naive-bayes-classifier.

This recipe shows how to run the Naive Bayes classifier on the weather dataset using the Naive Bayes classifier algorithm available in the Spark MLlib package. The code is written in...