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
