On 12/12/15 18:21, John Hardin wrote:
On Sat, 12 Dec 2015, Sebastian Arcus wrote:
One of my servers received a spam message which SA missed, with the
following report:
-0.4 AWL AWL: Adjusted score from AWL reputation
of From: address
After learning the messages as spam into bayes with sa-learn, I get
the following report:
-6.1 AWL AWL: Adjusted score from AWL reputation
of From: address
Luckily the message is now flagged as spam because I have manually
turned up the score on my BAYES_99 and BAYES_999 awhile ago. But what
intrigues me is that now the AWL module gives it a -6.1 score. Why
would AWL now tilt things heavily towards ham, after the message has
just been learned as spam? It seems to be making things worse instead
of better. Unless I am misunderstanding what AWL is supposed to be
doing?
You are. The name is misleading. AWL is more a score averager than a
whitelist. It's intended to allow for the occasionally spammy-looking
email from a historically hammy sender (and vice versa).
It has nothing to do with training, which only affect Bayes.
Messages from that sender will get negative AWL scores for a while
until their traffic history becomes more on the "spam" side.
OK - that's kind of what I assumed. What I don't understand is why the
AWL score changes after the message has been learned into the Bayes
database - and by so much?
