On Tue, 15 Mar 2016, Ted Mittelstaedt wrote:
On 3/15/2016 6:26 PM, John Hardin wrote:
On Tue, 15 Mar 2016, Ted Mittelstaedt wrote:
> > we have scripts checking any samples against current bayes
> > classification and ignore them if they already have BAYES_99,
>
> Is this even necessary? I thought the learner automatically
> rejected everything already tagged.
Already *learned*. There's nothing preventing you from learning messages
that scored BAYES_999 (or BAYES_00).
How exactly would it be a bad thing to learn as spam, spam that had this
score? (spam that had been verified, by hand, to be spam - or spam that
arrived at a honeypot address where it would be impossible for it to be
legit)
It wouldn't.
How would it be a bad thing to learn a piece of spam that had already
been caught by another rule and tagged as spam?
It wouldn't.
I guess my question is - if I have a piece of spam - a piece of mail that I
am absolutely positive is real, honest to God spam - not a
possible false positive - but real spam - how would it be bad in any
way to feed that into the learner as spam?
It's not.
I was replying to your statement "I thought the learner automatically
rejected everything already tagged" and interpreted "tagged" to mean "hit
BAYES_99 etc.". The learner doesn't care what rules the message you give
it may have hit, it just cares about whether or not it's already learned
it.
You can re-learn a message to switch it between ham and spam, but you
can't "learn it twice" to multiple-count the tokens.
Apologies if I misinterpreted what you were saying.
--
John Hardin KA7OHZ http://www.impsec.org/~jhardin/
jhar...@impsec.org FALaholic #11174 pgpk -a jhar...@impsec.org
key: 0xB8732E79 -- 2D8C 34F4 6411 F507 136C AF76 D822 E6E6 B873 2E79
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