Hi, The sa-learn instruction trains the bayes database; without it bayes will not tag any messages. You need to do the training with at least 200 spam and 200 ham. Be very careful that the messages in each are correct, so no spam in the ham folder.
There are options to learn from the mbox format see http://spamassassin.apache.org/full/3.1.x/dist/doc/sa-learn.html for full instructions and options. Any spam SA has already caught will have been caught through the additional tests SA runs. Bayes is a powerful tool and not one to discard lightly (imo) HTH Nigel On Mon, 31 Jul 2006 11:26:03 +0100, "Kailash Vyas" <[EMAIL PROTECTED]> wrote: >Hi all, > >I am running SpamAssassin version 3.1.4. >How do I make spamassasin learn. I have been reading about sa-learn >where I am supposed to run sa-learn on a spam folder. But why should I > run it on spam folder as i would assume that it should already be in >spamassasin database as spamassasin has already marked it as spam and >autolearn is on. how do I make spamassasin learn junk messages which >are not marked as spam. > >Also if I have collected all spam in a junk folder and run sa-learn on >this will it affect globally and show it as spam for all the users. I >ran sa-learn on a junk folder which I had collected from my >thunderbird junk folder. I got this message though after running it >even though there are around 200 messages in this folder. >Learned tokens from 0 message(s) (0 message(s) examined) > >I ran sa-learn --dump magic command to find out how much spamassasin >has learned and it shows this output > >0.000 0 3 0 non-token data: bayes db version >0.000 0 0 0 non-token data: nspam >0.000 0 0 0 non-token data: nham >0.000 0 0 0 non-token data: ntokens >0.000 0 0 0 non-token data: oldest atime >0.000 0 0 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 >co > >Please advise, > > >Thanks, >Kailash
