>>> Hello, >>> Why BAYES_99 have only the score 3.5 while 5.0 is required to identify a >>>mail >>> as spam? I think this rule should have a score about 5.1 (or anything >>>greater >>> than 5.0). >>> Because it's baye_99 not bayes_100. >>> ie: it's not 100% accurate.
> FWIW, I increased my bayes 95 and 99 rules to the same as the > 2.x releases. It has worked great at the stock spams (many of > which are caught on bayes 99 alone). The 'wrote:' and the > newer 'It's <name> :)' are being easily caught with bayes 99 > as well :) > I guess YMMV of course, but it's worked well here w/o the need > to come up with custom rules every time some new spammer trick > rolls around. Ok, I have a question on these Bayes rules related to false positives. It appears that many of my users are having legitimate emails scored in the 8 to 9 range. These emails are scoring high basically because they are hitting on one of the various Bayes rule (most notably the Bayes_50_Body and the Bayes_95_Body rules). Is there something straightforward that can be done to stop these legitimate scores from scoring high on the Bayes rules? I have already decreased the Bayes_50_Body rule from 5.0 to 2.5. I don't want to decrease the scores with every Bayes rule because I think I will start seeing some true spam delivered because it did not score high. Any ideas? Steve Ingraham
