What are the Pros and Cons of Naive Bayes?

Data Science, AI and ML
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#1

Pros:

  • It is easy and fast to predict class of test data set. It also perform well in multi class prediction
  • When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
  • It perform well in case of categorical input variables compared to numerical variable(s). For numerical variable, normal distribution is assumed (bell curve, which is a strong assumption).

Cons:

  • If categorical variable has a category (in test data set), which was not observed in training data set, then model will assign a 0 (zero) probability and will be unable to make a prediction. This is often known as “Zero Frequency”. To solve this, we can use the smoothing technique. One of the simplest smoothing techniques is called Laplace estimation.
  • On the other side naive Bayes is also known as a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.
  • Another limitation of [Naive Bayesis the assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent.