On Mon, Sep 16, 2013 at 1:38 PM, Harry Putnam <[email protected]> wrote: > > Yes, here is an example of a message rated as spam: > > X-Spam-Report: * 3.5 BAYES_99 BODY: Bayes spam probability is 99 to 100% > * [score: 0.9999]
OK, so you've got a BAYES_99 on that message, which is a pretty good indication that the training has worked. However, SA's confidence in the Bayes algorithm is only worth about one point out of a necessary five, so the rest of the rules have to contribute the other (just a bit more than) four points, and they do not: > * 0.4 STOX_REPLY_TYPE STOX_REPLY_TYPE > * 1.2 RCVD_NUMERIC_HELO Received: contains an IP address used for > HELO > * 1.8 STOX_REPLY_TYPE_WITHOUT_QUOTES STOX_REPLY_TYPE_WITHOUT_QUOTES This could be because the scores are tuned to include network tests which aren't able to be applied to your archive, or some such. In any case it's not the training that is failing you here. You have a couple of choices. You can assign your own higher score to the BAYES_99 rule in your local spamassassin config, or you can modify your procmail recipe to look for BAYES_99 in the filtered message and treat messages that have it as spam even if they do not score above the five point threshold. Anything that's falsely BAYES_99 is probably something you want to re-learn as ham anyway.
