MultinomialNB or GaussianNB or CategoricalNB what to use here?

Question

Let I have a input feature X = {X1, X2}. Where X1 is real-valued (also consider it follows Gaussian Dist) but X2 is a categorical feature. Now if I want to use the Naive Bayes algorithm. Which one I should use? Another way Does GaussianNB works perfect in Categorical features?

Answer 1

Transform your categorial feature X2 using get_dummies() (pandas library). And then train the model.

I recommend first try GaussianNB, an evaluate his accuracy. Then try others Naïve Bayes models that sklearn has. Without seeing the data (even having it) is quiet difficult to predict which model works betters in each case. Evaluate each one.