What are the advantages of using a naive Bayes for classification?

  • Very simple, easy to implement and fast.
  • If the NB conditional independence assumption holds, then it will converge quicker than discriminative models like logistic regression.
  • Even if the NB assumption doesn’t hold, it works great in practice.
  • Need less training data.
  • Highly scalable. It scales linearly with the number of predictors and data points.
  • Can be used for both binary and mult-iclass classification problems.
  • Can make probabilistic predictions.
  • Handles continuous and discrete data.
  • Not sensitive to irrelevant features.