Consistently, we develop complex and complex softwares, and consistently, the winner comes out to be simple ones. Comprehensibility is an important criteria for a Machine Learning model. That is the reason we build simple, comprehensible and reasonable model as the baseline first. Then build more complex one on top of that and every time, do better than the baseline.

Naive Bayes is a probabilistic model that builds on top of a “naive” assumption that all features are conditionally independent. Not only, this is easy to understand and explain, but also very computationally efficient. And, Naive Bayes turns out to be better than random guess, almost always. However, I am not making a claim here that a baseline will always be Naive Bayes. All I say is, NB is a fantastic initial baseline model.