I was wondering, if I can apply naive bayes, to a regression problem and how will it be done. I have 4096 image features and 384 text features and, it won't be very bad if I assume independence between them. Can anyone tell me how to proceed?
Answer
Naive bayes is used for strings and numbers(categorically) it can be used for classification so it can be either 1 or 0 nothing in between like 0.5 (regression)
Even if we force naive bayes and tweak it a little bit for regression the result is disappointing; A team experimented with this and achieve not so good results.
Also in wikipedia naivebayes has closeness to logistic regression.
Relation to logistic regression: naive Bayes classifier can be considered a way of fitting a probability model that optimizes the joint likelihood p(C , x), while logistic regression fits the same probability model to optimize the conditional p(C | x).
So now you have two choices, tweak naive bayes formula or use logistic regression.
I say lets use logistic regression instead of reinventing the wheel.
References:
Wikipedia: https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Relation_to_logistic_regression
Naive Bayes Regression Experiment: https://link.springer.com/content/pdf/10.1023%2FA%3A1007670802811.pdf