Question

Does multicollinearity effects in Naïve Bayes? If yes/no then why?

Answer 1

here is your answer

Answer 2

Answer:

Multi collinearity is a condition when two or more variables carry almost the same information. This condition will allow the model to be biased towards a variable. On the other hand Naive Bayes algorithm uses the Bayes theorem of probability. it assumes that the presence of one feature does not affect the presence or absence of other feature no matter up to which extent the features are interrelated. So, multi collinearity does not affect the Naive Bayes.  

Naive Bayes performs well when we have multiple classes and working with text classification. Advantage of Naive Bayes algorithms are:It is simple and if the conditional independence assumption actually holds, a Naive Bayes classifier will converge quicker than discriminating models like logistic regression, so we would need less training data, and even if the Naive Bayes  assumption doesn’t hold, It requires less model training time

The main difference between Naive Bayes (NB) and Random Forest (RF) are their model size. Naive Bayes model size is low and quite constant with respect to the data. The NB models cannot represent complex behavior so it won’t get into over fitting. On the other hand, Random Forest model size is very large and if not carefully built, it results to over fitting. So when a data is dynamic and keeps changing NB can adapt quickly to the changes and new data while using a RF you would have to rebuild the forest every time something changes.

Explanation: