> And after typing all this I'm thinking you might be right. But part of
> this approach is to run all these rules in YES/NO fashion and see if the
> probability is significant. For example: If I tested for SOME_TEST=NO
> and found it was scoring a probability of ~0.500 then it's indisputable
> that you are right.

Well, this still doesn't make any real sense to me; it seems equivalent to
the attempts at bayes poison that spammers stick into their spams: a bunch
of words totally unrelated to the mail in the hopes of outweighing the
useful terms. Now their trick works as a good spam indication because the
words they pick aren't common to my ham mails, so it is really a good spam
indication rather than poison. I'm not immediately convinced that will hold
for the usage you intend. Maybe. Maybe not.

However, if you want to do this, remember that bayes works on tokens and has
a tokenizer. So SOME_RULE=YES is probably either two or three tokens, and
you will end up scoring on the probability of YES and NO, along with the
frequency of the rule names, which will be 1. So you probably want to do
NO_SOME_RULE and YES_OTHER_RULE or the like when you build the insert list.
Again though I'm not sure I see the point in the yes and no factors; the
presence or absense of a word in the mail seems like a pretty good yes/no
indication to me.

Were I doing it I'd try it both ways and see if there is any difference in