* "Keith C. Ivey" <[EMAIL PROTECTED]> [2004:02:25:22:03:46-0500] scribed: > Michael D Schleif <[EMAIL PROTECTED]> wrote: > > > pts rule name description > > ---- ---------------------- > > -------------------------------------------------- > > 50 MDS_Remove_Subject MDS - un-subscribe idiocy > > -0.9 BAYES_10 BODY: Bayesian spam probability is 10 to 20% > > [score: 0.1441] > > -24 AWL AWL: Auto-whitelist adjustment > > Okay, so you have an insanely high AWL adjustment because > you've set up a rule with an insanely high score. Even so, the > adjustment only brings the score down to 25, which presumably > is still far above the spam threshold, so what's the problem?
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. NOTE: I raised scores of MDS_Remove_Subject, and its ilk, in response to AWL scores that continually dragged these messages from the ranks of spam into the ranks of ham. Of course, I did this before I better understood the AWL process. Perhaps, I should never have used AWL -- perhaps, procmail is a better spam fighting tool than spamassassin ;> 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. Unfortunately, this thread has only driven me from AWL, because its implementation appears skewed, especially in mailing list context. And, nobody has offered evidence to the contrary. Nor has anybody offered a way to reconfigure my AWL to provide value to my environment. I can live without AWL, and I am doing so now. Have a great day! -- Best Regards, mds mds resource 877.596.8237 - Dare to fix things before they break . . . - Our capacity for understanding is inversely proportional to how much we think we know. The more I know, the more I know I don't know . . . --
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