Naive about which Naive Bayes in article
First off, yes there are different Naive Bayes algorithms. But they are all based on the same principle, namely Bayes Theorem where the features are assumed to be independent.

Here is a short guide on when to use which for spam detection (or document classification in general for that matter):


Bernoulli Naive Bayes


  Each e-mail is represented as a binary vector, and in this case, whether a word is present in the e-mail matters as much as if it isn't. For instance, the fact that "viagra" is in the e-mail might mean it's "spam" and the fact it isn't might mean it's "not spam"

Multinomial Naive Bayes with boolean features


  Each e-mail is represented as a binary vector, and in this case, whether a word is present in the e-mail matters more than if it isn't. For instance, if "grandma" is in the e-mail, it might mean that it's "not spam", but if it isn't in the e-mail, it doesn't necessarily mean that it's "spam".

Multinomial Naive Bayes with term frequency


  Each e-mail is represented by how many times each word occurs.

Gaussian Naive Bayes


  This deals with continuous values, so this isn't applicable here.