# Independence of Features assumption in Naive Bayes

How do we know if your features in my dataset are independent before applying Naive Bayes? Basically I want to know is it possible for us to get an idea before training our model if Naive Bayes will give decent results on it.

Statistical independence is a pretty straightforward thing. If $$p(A\cap B) = p(A) p(B)$$ then $$A$$ and $$B$$ are independent (in other words if marginal distributions are equal to conditional). If you want, you could even check that on your data. Though it would be easier to check: $$p(A|B) = p(A) \ \text{and} \ p(B|A) = p(B)$$ instead of constructing a joint distribution. The latter is easy, if your features are categorical then you could estimate $$p(A)$$, $$p(B)$$ , $$p(A|B)$$, $$P(B|A)$$ as sample frequencies. If one of A or B is categorical computations are also simple. If both A and B are numeric, you need to fit a KDE (kernel density estimation) model to all probability distributions.