On Wed, 2005-03-30 at 15:27 -0600, Matthew Lenz wrote: [snip spam info] > Ideas where to start (other than having her change her email address > hehe)
It doesn't look like you are using any of the SARE rulesets. There are 3 things I would do to start off... First, assuming that the 5000 messages that you classified as spam have been verified to actually be spam, I would run them through sa_learn so that bayes can learn from its mistakes. Second, if you haven't done so already, I would decrease the score that is assigned to the rule ALL_TRUSTED. My false negatives were helped greatly from this. Finally, I would look at using some of the SARE rulesets and SURBL. You may want to take a look at the Other Rules page as well, several rulesets on that page (especially backhair.cf, chickenpox.cf, weeds.cf, and 99_FVGT_Tripwire.cf) do a wonderful job at pushing those spams that are on the border over to the tagged/deleted side. Tim Donahue
