Mail::SpamAssassin::PluUgsienr::CAoMunattiorlLi:eb:auSrtpneaTdmhArPseessrahlsoslDidon(c:3u:pmP)elnutgaitni:o:nAutoLearnThreshold(3p)



NNAAMMEE
       Mail::SpamAssassin::Plugin::AutoLearnThreshold - threshold-based
       discriminator for Bayes auto-learning

SSYYNNOOPPSSIISS
         loadplugin     Mail::SpamAssassin::Plugin::AutoLearnThreshold

DDEESSCCRRIIPPTTIIOONN
       This plugin implements the threshold-based auto-learning discriminator
       for SpamAssassin's Bayes subsystem.  Auto-learning is a mechanism
       whereby high-scoring mails (or low-scoring mails, for non-spam) are fed
       into its learning systems without user intervention, during scanning.

       Note that certain tests are ignored when determining whether a message
       should be trained upon:

       o   rules with tflags set to 'learn' (the Bayesian rules)

       o   rules with tflags set to 'userconf' (user configuration)

       o   rules with tflags set to 'noautolearn'

       Also note that auto-learning occurs using scores from either scoreset 0
       or 1, depending on what scoreset is used during message check.  It is
       likely that the message check and auto-learn scores will be different.

UUSSEERR OOPPTTIIOONNSS
       The following configuration settings are used to control auto-learning:

       bayes_auto_learn_threshold_nonspam n.nn   (default: 0.1)
           The score threshold below which a mail has to score, to be fed into
           SpamAssassin's learning systems automatically as a non-spam
           message.

       bayes_auto_learn_threshold_spam n.nn      (default: 12.0)
           The score threshold above which a mail has to score, to be fed into
           SpamAssassin's learning systems automatically as a spam message.

           Note: SpamAssassin requires at least 3 points from the header, and
           3 points from the body to auto-learn as spam.  Therefore, the
           minimum working value for this option is 6.

       bayes_auto_learn_on_error (0 | 1)        (default: 0)
           With "bayes_auto_learn_on_error" off, autolearning will be
           performed even if bayes classifier already agrees with the new
           classification (i.e.  yielded BAYES_00 for what we are now trying
           to teach it as ham, or yielded BAYES_99 for spam). This is a
           traditional setting, the default was chosen to retain backwards
           compatibility.

           With "bayes_auto_learn_on_error" turned on, autolearning will be
           performed only when a bayes classifier had a different opinion from
           what the autolearner is now trying to teach it (i.e. it made an
           error in judgement). This strategy may or may not produce better
           future classifications, but usually works very well, while also
           preventing unnecessary overlearning and slows down database growth.



perl v5.12.2                Mail::2S0p1a1m-A0s6s-a0s6sin::Plugin::AutoLearnThreshold(3p)
