-Niels Bohr

Machine learning (ML) in combination with big data is a radical combination that has created some great impacts in the field of research in Academia and Industry. Moreover, many research areas are also entering into big data since datasets are being generated and produced in an unprecedented way from diverse sources and technologies, commonly referred as the Data Deluge. This imposes great challenges on ML, data analytics tools, and algorithms to find the real VALUE out of big data criteria such as volume, velocity, and variety. However, making predictions from these huge dataset has never been easy.

Considering this challenge, in this chapter we will dig deeper into ML and find out how to use a simple yet powerful method to build a scalable classification model and even more. In a nutshell, the following topics will be covered throughout this chapter:

• Multinomial classification
• Bayesian inference
• Naive Bayes
• Decision trees
• Naive Bayes versus decision trees