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?
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.