Ok, I see how it's happening.
Now, 'why'? Can you point me to some documentation that explains
why it's desirable to ignore certain types of tests? Maybe I'm being thick,
but I'm not finding it in the Wiki or docs.
Thanks,
Chris
-----Original Message-----
From: Theo Van Dinter [mailto:[EMAIL PROTECTED]
Sent: Tuesday, September 07, 2004 2:41 PM
To: Chris Blaise
Cc: users@spamassassin.apache.org
Subject: Re: Bayes scoring weirdness?
On Tue, Sep 07, 2004 at 02:32:42PM -0600, Chris Blaise wrote:
> The rules were ALL_TRUSTED,MISSING_DATE,USER_IN_BLACKLIST and I
think
> since "ALL_TRUSTED" is a negative value.
>
> Am I missing something about how auto-learn should consider this?
>
> Is there a reason why it doesn't consider such a message as spam,
> soley on the score? I realize that it won't learn spam if the header
> and body aren't at least 3 each, but for such a high score, it seems
> like it should be able to disregard that to say, "This is huge; learn it
as spam."
This has been convered many times. It's probably in the wiki, and
definitely in the documentation:
[...]
bayes_auto_learn ( 0 | 1 ) (default: 1)
[...]
Note that certain tests are ignored when determining whether a
mes-
sage should be trained upon:
- rules with tflags set to 'learn' (the Bayesian rules)
- rules with tflags set to 'userconf' (user white/black-listing
rules, etc)
- rules with tflags set to 'noautolearn'
Also note that auto-training occurs using scores from either
score-
set 0 or 1, depending on what scoreset is used during message
check. It is likely that the message check and auto-train scores
will be different.
As always though, run with -D and you'll find out plenty. ;)
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