I am trying to understand inner workings of spam assassin and would be great if someone can answer my questions. I have read online documentation but there are still some questions left unanswered or I am not sure about.
As far as I understand, the default configuration of spamassassin processes emails in this fashion DNSBL Tests ---> RAW Body Tests ---> Bayesian Learning --> AWL [Is the sequence right? I know for sure AWL comes in last, what about Bayesian learning and RAW Body tests' order? Did I miss any module?] Why do we need Bayesian learning in presence of RAW body tests? Mails which have very high or very low score are fed to bayesian learning. Since we are confident about them being HAM or SPAM what do we want to learn from them - The regex filters have identified that the mail is a spam (say), what additional does bayesian learning achieve? Does it learn other words in the spam mail (say words surrounding obfuscated term) in hope of matching them in future emails? Or am I understanding it completely different? Thnx for help. -- View this message in context: http://www.nabble.com/Understanding-SpamAssassin-tp25530437p25530437.html Sent from the SpamAssassin - Users mailing list archive at Nabble.com.
