In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Wikipedia

Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predcitors) in a learning problem. Maxumum-likelihood training can be done by evaluting a closed-form exporession, which takes linear time, rather tahn by expensive iterative approximation as used for many other typs of classifier. Wikipedia

If you ask yourself what’s the most important thing in machine learning, what’s your answer? All data scientist would have different answers.