Hi,
I have a couple of installs where SpamAssassin is happily filtering mail more or less correctly however I noticed that it does not seem to be autolearning. How do I make it autolearn? Here is one of the headers: X-Spam-Status: Yes, hits=31.0 required=5.0 tests=BANG_EXERCISE,BANG_GUARANTEE,\n\tCLICK_BELOW_CAPS,DATE_IN_FUTURE_03_06,DATE_SPAMWARE_Y2K,\n \tFORGED_MUA_OUTLOOK,FORGED_OUTLOOK_TAGS,GUARANTEED_STUFF,HTML_50_60,\n \tHTML_FONTCOLOR_BLUE,HTML_FONTCOLOR_GREEN,HTML_FONTCOLOR_RED,\n\tHTML_FONT_BIG,HTML_IMAGE_ONLY_08,HTML_LINK_CLICK_CAPS,\n \tHTML_LINK_CLICK_HERE,HTML_MESSAGE,HTML_MIME_NO_HTML_TAG,IMPOTENCE,\n \tMIME_HTML_NO_CHARSET,MIME_HTML_ONLY,MIME_HTML_ONLY_MULTI,\n\tMISSING_MIMEOLE,MONEY_BACK,PENIS_ENLARGE,PENIS_ENLARGE2 autolearn=no \n\tversion=2.63
Why doesn't autolearn=yes?
This is a fresh install using the defaults. Slackware 9.1 SpamAssassin installed through Perl. Summary of my perl5 (revision 5.0 version 8 subversion 3) configuration:
Don't worry about that. I wondered about the same behavior and I found the explanation somewhere in spamassassin docs. I've the debug mode of spamassassin on in one server look below
This messages was autolearned:
spamd[23566]: debug: auto-learn? ham=-0.2, spam=15.7, body-hits=9.125, head-hits=29.581
spamd[23566]: debug: auto-learn: currently using scoreset 3. recomputing score based on scoreset 1.
spamd[23566]: debug: auto-learn: original score: 30.782, recomputed score: 28.311
spamd[23566]: debug: auto-learn? yes, spam (28.311 > 15.7)
And this one not, even its score was 19.067 greater than 15.7:
spamd[24686]: debug: auto-learn? ham=-0.2, spam=15.7, body-hits=7.958, head-hits=13.576
spamd[24686]: debug: auto-learn: currently using scoreset 3. recomputing score based on scoreset 1.
spamd[24686]: debug: auto-learn: original score: 19.067, recomputed score: 9.487
spamd[24686]: debug: auto-learn? no: inside auto-learn thresholds
I found that body-hits and head-hits must be greater than some value, otherwise the message it is not autolearned. Search in the spamassassin docs.
Cheers
Salvatore
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