On 3/20/2017 6:37 AM, Bernard wrote:
Thanks for that information.
After ~1750 messages having been digested, still no improvement:
0.000 0 3 0 non-token data: bayes db version
0.000 0 23 0 non-token data: nspam
0.000 0 1729 0 non-token data: nham
0.000 0 123471 0 non-token data: ntokens
0.000 0 1358530476 0 non-token data: oldest atime
0.000 0 1489938564 0 non-token data: newest atime
0.000 0 0 0 non-token data: last journal
sync atime
0.000 0 0 0 non-token data: last expiry atime
0.000 0 0 0 non-token data: last expire
atime delta
0.000 0 0 0 non-token data: last expire
reduction count
Have you got an idea of the required order of magnitude of the input
volume for the bayesian filter to kick in?
---
Bernard
On 20/03/2017 11:15, Reindl Harald wrote:
Am 20.03.2017 um 11:12 schrieb Bernard:
1. How come the same message being classified either as spam/ham
returns the same score? I would expect a message learnt as
'spam' to
get a score at least equal to the spam score threshold
2. Even though the message was correctly learnt as spam before and
after the test, receiving this email message is still not tagged as
spam:
X-Spam-Checker-Version: SpamAssassin 3.4.0 (2014-02-07) on ***
X-Spam-Level: **
X-Spam-Status: No, score=2.1 required=5.0
tests=MISSING_HEADERS,SPF_FAIL,
SPF_HELO_FAIL autolearn=no autolearn_force=no version=3.4.0
Am I missing something?
yes, tarin your bayers properly with enough spam *and* ham samples
and train the bayes wihich is really in use - do you see any BAYES_
tag above? no! so bayes was not used at all
You need to train more than 23 messages as ham first. Read the
documentation in the SA manpages and on the wiki to make sure you meet
every criteria for running bayes.
