On Sunday, November 10 2013, Karsten Bräckelmann wrote: > nham is the "Number of HAM" learned, in messages. Same for nspam. Keep > training until both are at least 200 -- accuracy should improve > dramatically after that.
I figured that out. > Keep an eye on the X-Spam-Status header, autolearn bit. > > If that happens frequently for FNs, there's a problem somewhere. We'd > need the X-Spam headers and preferably the full, raw message put up a > pastebin for debugging. After some initial training. For all messages that I received since I started using SA (about 20 messages, of which 5 were false-negatives, and the rest were true-negatives), autolearn seems to be working OK, i.e., when messages score below the threshold, autolearn works, and when messages score above the threshold, I see "autolearn=no". > There's one thing worrying in your comment: "whether false-negative or > true-negative". You DO have spam also, right? I mean, classified spam is > not just silently discarded without you ever seeing it? That would be > really bad at this stage. Take it, verify it, learn it. I do receive spam. About 1 or 2 per day. But so far SA hasn't been able to catch any of them, and all spam I receive has been marked as ham so far. The message headers are OK, there is nothing apparently wrong with SA, but it is just not catching most of my spam. I assume this is normal behavior since I just started using SA a few days ago. For every spam message that I received, I analyze its headers, verify that everything is OK with SA, and then feed it to sa-learn. -- Sergio
