Re: Whitelist or BAYES?
Bill Cole <[email protected]>
| Newsgroups | gmane.mail.spam.spamassassin.general |
|---|---|
| Message-ID | <[email protected]> |
On 2024-09-30 at 16:22:49 UTC-0400 (Mon, 30 Sep 2024 16:22:49 -0400) joe a <[email protected]> is rumored to have said: > On 9/27/2024 04:05:51, Matus UHLAR - fantomas wrote: >> On 26.09.24 10:27, joe a wrote: >>> Maybe I should not ask this, but . . . >>> >>> A relatively innocuous member informational email from a local town >>> Library (monthly) gets marked as spam as shown below. >>> The BAYES_99 and BAYES_999 values are something I am toying with for >>> other reasons. Seems odd these should hit either one of those >>> tests. >>> >>> So, on the one hand I can add them to whitelist and be done with it, >>> or I can add >>> them to missed HAM for re-learning. >>> >>> Which is the best approach? >> >> so far, both. You may need to relearn multiple their (monthly) mails >> before it has effect. >> >>> X-Spam-Report: >>> * 4.1 BAYES_99 BODY: Bayes spam probability is 99 to 100% >>> * [score: 1.0000] >>> * 5.0 BAYES_999 BODY: Bayes spam probability is 99.9 to >>> 100% >>> * [score: 1.0000] >> >> You have raised BAYES_99 and BAYES_999 to huge values so I recommend >> to rethink that. >> > You some "don't because" examples? Seems to me, off hand, that if > it's 99% or 99.9% then a high value does no harm. Perhaps half what > I have would be sufficient though. Bayes is a statistical method and so will always make some errors, as in this case. BY DEFINITION, one in a hundred messages hitting BAYES_99 will be ham, as will one in a thousand that hits BAYES_999. I can't claim that the default scores are the best possible ones, but they don't result in many false positive *final scores* for most people. -- Bill Cole [email protected] or [email protected] (AKA @[email protected] and many *@billmail.scconsult.com addresses) Not Currently Available For Hire