> This is what I do from .procmailrc.  I have autolearn turned off.
> 
> I have a Junk Maildir and Junk/Bad Maildir nested inside of Junk.  I  
> never verify things in Bad, and verify everything in Junk.   I can  
> manually learn non-Bad things in Junk by dragging to one of my Auto  
> folders.  I have Auto/Ham, Auto/HamBayes, Auto/Spam, Auto/SpamBayes.   
> A cronjob will take things in these dirs and if they are Ham,  
> classify them as such and redeliver them to my Inbox, Spam gets  
> classified as Spam and moved to a non-mail directory to be archived.   
> The "Bayes" folders are for classifying but not scoring (using a  
> homegrown stats program... can't have my numbers getting thrown off).
> 
> # if spamassassin thinks it's > 10, lets classify it as spam and mark  
> bad
> :0
> * ^X-Spam-Checker-Version: SpamAssassin .* on .*\.myhost\.com
> * ^X-Spam-Level: \*\*\*\*\*\*\*\*\*\*
> {
>          # learn as spam if > 10 and not image spam
>          :0c
>          * ^X-Spam-Status: .*autolearn=disabled.*
>          * !^Content-Type:.*(multipart/related).*
>          | $HOME/local/bin/sa-learn --spam --no-sync
> 
>          # score as successful spam catch
>          :0ci
>          | $HOME/bin/sa_score.pl -s
> 
>          # put in Bad Junk dir.
>          :0
>          /home/bdwilson/Maildir/.Junk.Bad/
> }
> 
> # if < 10 in SA score, don't bayes learn and only put in Junk.
> # user can drag to Auto/SpamBayes if it is truly spam to get added to
> # bayes without affecting sa_score stats.
> :0
> * ^X-Spam-Checker-Version: SpamAssassin .* on .*\.myhost\.com
> * ^X-Spam-Status: Yes
> {
>          # score as spam catch
>          :0ci
>          | $HOME/bin/sa_score.pl -s
> 
>          # only put in Junk
>          :0
>          /home/bdwilson/Maildir/.Junk/
> }
> 
> # everything else I manually mark as ham. 
>   


All that is ok for end user training bayes
But an ISP with 7Million mails a day for ~1000 domains  cannot do that.
Autolearn is the option I can see.
What I wanted to know was Can I do an optional autolearn 





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