Gustafson, Tim wrote:
0.000 0 2 0 non-token data: bayes db version 0.000 0 88033 0 non-token data: nspam 0.000 0 15592 0 non-token data: nham 0.000 0 1729756 0 non-token data: ntokens 0.000 0 1010964573 0 non-token data: oldest atime 0.000 0 1762110386 0 non-token data: newest atime 0.000 0 1101309901 0 non-token data: last journal sync atime 0.000 0 1101301792 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 count
I agree with Jim that having your SPAM/HAM numbers match doesn't really matter, as long as you have sufficient amounts of each. I think the "threshold" where my users started to expierence the best filtering accuracy was when I topped 1000 SPAMs and HAMs. But, as Jim said before, your mileage may vary.
Since we're all playing show-and-tell, here is a dump of the magic on my company's mail server.
0.000 0 3 0 non-token data: bayes db version
0.000 0 101024 0 non-token data: nspam
0.000 0 164343 0 non-token data: nham
0.000 0 240026 0 non-token data: ntokens
0.000 0 1101226944 0 non-token data: oldest atime
0.000 0 1101313137 0 non-token data: newest atime
0.000 0 1101313136 0 non-token data: last journal sync atime
0.000 0 1101270336 0 non-token data: last expiry atime
0.000 0 43200 0 non-token data: last expire atime delta
0.000 0 196502 0 non-token data: last expire reduction count
My auto-learn thresholds are set as follows in the global local.cf.
bayes_auto_learn_threshold_nonspam 0.8
bayes_auto_learn_threshold_spam 10.0It is very important that you keep your bayes_min_[ham|spam]_num settings to at least 1000.
-- Matt Barton Webexcellence PH: 317.423.3548 x22 TF: 800.808.6332 x22 FX: 317.423.8735 [EMAIL PROTECTED] www.webexc.com
