On 10/1/2024 8:58 AM, Bill Cole wrote:
On 2024-09-30 at 16:22:49 UTC-0400 (Mon, 30 Sep 2024 16:22:49 -0400)
joe a <joea-li...@j4computers.com>
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.
Also, keep in mind that BAYES_999 is an add-on to BAYES_99. Any message
that hits BAYES_999 will also hit BAYES_99. That is why the default
score for BAYES_999 is only 0.2.
The way you have your scores set will ensure that any message that hits
BAYES_999 will get 9.1 points added (4.1 + 5.0). This may or may not
work for you, but you should be aware of it.
--
Bowie
