Michael D Schleif <[EMAIL PROTECTED]> wrote: > The problem is in the scores that I cannot present -- those that I have > -R'd and redisposed -- as I said. Those did *NOT* use > MDS_Remove_Subject; but, were from same mailing lists. They *ARE* spam; > but, scored as ham.
They also wouldn't have had an adjustment of -25, which is what people have been surprised by in your description of your situation. The -25 results from the very high spam score caused by the 50-point rule. Without it, the adjustment would be much less -- though of course it will certainly sometimes cause messages' scores to be moved from spam range to the nonspam range. > Nevertheless, skewing my point by taking only one (1) of my examples > does not bolster the value of AWL. In point of fact, I have witnessed > AWL -- as you call it, `insanely high AWL adjustment' -- skew mailing > list messages, and I began this thread in hopes of understanding how to > avoid this and to use AWL to my benefit. And people have wanted more information about your situation, because they haven't experienced anything like the problems you have. Thank you for posting the examples. I apologize for selecting one of them, but I didn't understand why you were posting it or the other four high-scoring examples you posted. They were classified as spam, so the system seems to be working as intended in those cases (I'm assuming you've already read http://wiki.spamassassin.org/w/AwlWrongWay ). Along with those five examples of correctly identified spams, you did post two examples where the AWL adjustment may have caused a false negative. But in both cases the message triggered BAYES_00, which probably had a larger contribution to the miscategorization. Judging by those I'd say the real problem is spam being incorrectly autolearned as ham. Spam should not be getting BAYES_00 or BAYES_01, as it is in the majority of your examples. Maybe you should be blaming autolearning rather than autowhitelisting. -- Keith C. Ivey <[EMAIL PROTECTED]> Washington, DC
