uhlar at fantomas
Mar 9, 2012, 7:38 AM
Post #22 of 37
>On Fri, 9 Mar 2012 08:38:21 +0100
Re: Allowing IMAP users to train spam/ham
[In reply to]
>Matus UHLAR - fantomas wrote:
>> >> On 05.03.12 12:15, RW wrote:
>> >> >I don't like it. It relies on FPs being removed from the SPAM
>> >> >folder rather than spam being sent to a learn-spam folder.
>> >On Wed, 7 Mar 2012 15:35:05 +0100
>> >Matus UHLAR - fantomas wrote:
>> >> Pardon me, but:
>> >> Usage for end users
>> >> *move mail into SPAM folder to classify as spam
>> >> *move mail out of SPAM folder to classify as not spam
>> >> isn't the former what you want?
>> On 07.03.12 21:44, RW wrote:
>> >I'm more concerned about what happens to the mail that isn't moved.
>> apparently nothing, because it is assumed to be correctly evaluated.
On 09.03.12 14:13, RW wrote:
>So are you saying that a legitimate mail that hits BAYES_99 and
>scores 4.9 isn't worth learning as ham because it's correctly evaluated.
It's easier - it takes less CPU time and users' effort.
It's alsu MUCH more important to train FPs then train all.
>> >I think positive training is better than supervised autolearning
>> those above clearly indicate postive and negative trainin, or do you
>> have different informations?
>When I first looked at it, it retrained on errors, with DSPAM
>autotraining on everything. It probably does support train-on-error,
>but IMO it would be inappropriate to train Bayes that way.
You can of course configure mailer to train automatically on anything
received/delivered. However this would apparently cause much more FP's
and FN's rate than letting user train only those that misfire.
>> >The scheme might work well for pure train-on-error, but that's not
>> >really practical on Spamassassin where the classification is
>> >distinct from the Bayes result.
>If you're going to train on error then train on the right error, not a
>rarer, correlated error.
The only error that really matters is the one that causes misfiring.
>The FP/FN rate based on the SA classification isn't anywhere near high
>enough to train BAYES. If a user receives 10 legitimate mails a day and
>SA works at its target FP rate of 1 in 2500, it would take over
>100 years for Bayes to even turn-on.
with FP rate of 1 in 2500, it will not matter that much :-)
But yes, this is one of weaknesses of bayes system. It requires
much mail to start firing. However you can lower both
bayes_min_ham_num and bayes_min_spam_num and they will start hitting
sooner. You can also modify autolearning scores although.
Matus UHLAR - fantomas, uhlar [at] fantomas ; http://www.fantomas.sk/
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