In the ungraded lab, it was said that the Naive Bayes would not perform so well for the given dataset distribution.

Here is what i think the reason is:

Since in Naive Bayes we’re basically learning the probability distribution of the inputs w.r.t classes and then we choose the class with the higher probability.

In the above cases since the two dataset have an overlaping region within

2*sigma for both class with lots of points inside it, so basically in that region if the positive class has higher probability say between (0.3-0.4) and negative class would also have a significant but lower probability range say (0.25-0.3) then inside that region we would always endup choosing positive class over negative, same case for negative class, and thus we would always ignore the other possible output which would reduce the accuracy since in that region there are a lot of points, while if there was no overlapping or if the number of points inside that region was low then such case would rarely arise.

Is my reasoning correct, or is there another reason for it?