Why does the naive bayes algorithm make the naive assumption that features are independent to each other?

Question

Naive Bayes is called naive because it makes the naive assumption that features have zero correlation with each other. They are independent of each other. Why does naive Bayes want to make such an assumption?

Answer 1

By doing so, the joint distribution can be found easily by just multiplying the probability of each feature whilst in the real world they may not be independent and you have to find the correct joint distribution. It is naive due to this simplification.

Answer 2

Naive bayes make such assumptions to simplify the calculations. You can take a look at bayesian belief network which do not make such assumptions

Answer 3

Just to complete the answers given and clarify them in some points: the assumption in Naïve Bayes is that features are conditionally independent given the predicted variable, not independent. Note also that, even though this simplification makes naïve assumptions about the conditional joint distribution of features that are in many cases far from the true distribution, our aim here is not to estimate probabilities but to perform a binary classification and, for that purpose, our simplification strategy may be good enough.