Hi,
Did you understand that all
tokens are learned, regardless whether they have been seen before?
That doesn't really matter from a user perspective, though, right? I
mean, if there are tokens that have already been learned are learned
again, the net result is zero.
Very much not zero. Each token has several values assocated with it:
# ham
# spam
time-stamp
So each time it's learned its respective ham/spam counter is incremented
which indicates how spammy or hammy a given token is and its time-stamp is
updated indicating how "fresh" a token is. The bayes expiry process removes
"stale" tokens when it does its job to prune the database down to size.
Ah, yes, of course. I knew about that, but somehow didn't put it
together with this.
I would like to know why, after training similar messages a number of
times, it still shows the same bayes score on new similar messages.
I'd also like to figure out why or how many more times it's necessary
for a message to be re-trained to reflect the new desired persuasion.
I've had this particular FN with frequently a bayes50, sometimes lower,
that also have a few dozen every day that are tagged as spam properly,
but still have bayes50. I pull them out of the quarantine and keep
training them as spam, but there's still a few that get through every day.
Is there any particular analysis I can do on one of the FNs that can
tell me how far off the bayes50 is from becoming bayes99 in a similar
message?
Hopefully that's clear. I understand there's a large number of variables
involved here, and I would think the fewer number of tokens in a
message, the more difficult it probably should be to persuade, but it's
frustrating to see bayes50 so repeatedly...
Thanks,
Alex
