Talk:Bayesian message classification

Article Checklist for "Bayesian message classification"

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Article status	External article: from another source, with little change
Underlinked article?	Yes
Basic cleanup done?	Yes
Checklist last edited by	Derek Harkness 04:15, 2 May 2007 (CDT)

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It's more complicated than that.

This article gives the impression that Bayesian spam filtering is done in a particular way, i.e. by treating probabilities of each word independently. That is not the only possible way to do Bayesian spam filtering, and I don't think it's the way it's usually (or always) done. Another way is to look at probabilities of phrases. Yet another way is to look at probabilities of certain combinations of words (regardless of where in the article the word appears). For example, the word "interest" might not by itself increase the spam score (or not much), but if it appears in the same message as "mortgage" and "house" it might add significantly to the probability of the message being classified as being about mortgages, and then get a Bayesian spam score based on the user's previous reactions to other messages about mortgages. In other words, it can be done in two steps, using Bayes' theorem at each step. --Catherine Woodgold 21:22, 2 May 2007 (CDT)