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- Do not train Bayes on different mail streams or public spam corpora. These methods will mislead Bayes into believing certain tokens are spammy or hammy when they are not.
- To train Spamassassin, you get a mailbox full of messages that you know are spam and use the sa-learn program to pull out the tokens and remember them for later:
sa-learn --showdots --mbox --spam spam-file
Then you get a mailbox full of messages you're sure are ham and teach Bayes about those:
sa-learn --showdots --mbox --ham ham-file
It is important to do both. - The bayesian classifier can only score new messages if it already has 200 known spams and 200 known hams.
- If Spamassassin fails to identify a spam, teach it so it can do better next time. Run it through the sa-learn program and it will be more likely to correctly identify it as spam next time. Likewise, if SA puts a ham in your spam folder, run that message through sa-learn --ham ham-folder.
- It's OK to feed emails with Spamassassin markup into the sa-learn command – sa-learn will ignore any standard Spamassassin headers, and if the original email has been encapsulated into an attachment it will decapsulate the email. In other words sa-learn will undo any changes which Spamassassin has done before learning the spam/ham character of the email.
- If you or any upstream service has added any additional headers to the emails which may mislead Bayes, those should probably be removed before feeding the email to sa-learn. Alternatively, use the bayes_ignore_header setting in your local.cf (as detailed in the man page for Mail::SpamAssassin::Conf).
- An example of a ham-file could be ~/mail/saved-messages, or wherever your email client saves messages. Make sure all spam is deleted before using sa-learn on a ham-file.
Similar to the training example above, for a maildir format mailbox, the commands should be altered as shown below.
For a mailbox you're sure contains only spam messages,
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- OBVIOUSLY – change the options at the very beginning.
2. The -z option to rsync automatically uses gzip compression; no need to do this first. Also it will only sync newly added parts of the file, it doesn't re upload the file everytime!
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If you have "maildir" mailboxes, running spamassassin -r multiple times can be tedious for large numbers of spam. So you can use this report_spam.pl script to run it for you. The script is written in perl. You can save the script to your spamassassin computer and then run it using report_spam.pl your_spam_directory. Each message in your_spam_directory will then be learned in bayes and reported to the checksum services.
(KurtYoder)