Re: Train Spamassassin

Am 05.01.2017 um 03:10 schrieb Ryan Butler:
> There's also a dbmail-export command isn't there to turn it into mbox files?

yes, but you need to fix your picture how to train SA *properly*

blowing each and every email there without 100% classification is the 
wrong way - every single missclassified message does that much harm that 
you need 10-20 correct ones fixing the damage

> On Wed, Jan 4, 2017 at 5:21 PM, Reindl Harald <[email protected]
> <mailto:[email protected]>> wrote:
>
>
>
>     Am 04.01.2017 um 22:32 schrieb Claas Kähler:
>
>         Okay this part is easy, but how could i extract samples from
>         dbmail to
>         put them into a folder?
>
>
>     by IMAP - you are not supposed to directyl access email via mysql
>     since you underestimate the complexity of the strcuture and must not
>     rely on internal aka non-public API's
>
>         Am 04.01.17 um 22:26 schrieb Reindl Harald:
>
>
>
>             Am 04.01.2017 um 20:50 schrieb Claas Kähler:
>
>                 Hallo everybody,
>
>                 I have a serious Spam-Problem and I want to feed
>                 sa-learn with those
>                 Spam-mails to get rid of it.
>
>                 Has someone found a solution to train Spamassassin with
>                 data from
>                 DBMail?
>
>
>             just put your samples in folders and use "sa-learn" with the
>             correct user
>
>
>             [root@mail-gw:~]$ bayes-stats.sh
>             0      81476    SPAM
>             0      25659    HAM
>             0    3227434    TOKEN
>
>             insgesamt 376M
>              24K -rw-r----- 1 sa-milt sa-milt  24K 2017-01-04 16:29
>             bayes_seen
>              65M -rw-r----- 1 sa-milt sa-milt  81M 2017-01-04 16:29
>             bayes_toks
>             312M -rw-r----- 1 sa-milt sa-milt 312M 2017-01-04 16:29
>             wordlist.db
>
>             BAYES_00         1682   58.99 %
>             BAYES_05           84    2.94 %
>             BAYES_20           81    2.84 %
>             BAYES_40          104    3.64 %
>             BAYES_50          394   13.81 %
>             BAYES_60           56    1.96 %     9.75 % (OF TOTAL BLOCKED)
>             BAYES_80           60    2.10 %    10.45 % (OF TOTAL BLOCKED)
>             BAYES_95           38    1.33 %     6.62 % (OF TOTAL BLOCKED)
>             BAYES_99          352   12.34 %    61.32 % (OF TOTAL BLOCKED)
>             BAYES_999         285    9.99 %    49.65 % (OF TOTAL BLOCKED)
>
>             DELIVERED        5104   91.91 %
>             DNSWL            4984   89.75 %
>             SPF              4088   73.61 %
>             SPF/DKIM WL      2250   40.51 %
>             SHORTCIRCUIT     2690   48.44 %
>
>             BLOCKED           574   10.33 %
>             SPAMMY            506    9.11 %    88.15 % (OF TOTAL BLOCKED)