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

       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:

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

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

       •   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.

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

         loadplugin     Mail::SpamAssassin::Plugin::AutoLearnThreshold

       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.

           If test option "autolearn_header" or "autolearn_body" is set, points from that  rule  are  forced  to
           count as coming from header or body accordingly.  This can be useful for adjusting some meta rules.

           If  the  test option "autolearn_force" is set, the minimum value will remain at 6 points but there is
           no requirement that the points  come  from  body  and  header  rules.   This  option  is  useful  for
           autolearning  with  rules  that  are considered to be extremely safe indicators of the spaminess of a
           message.

       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
           backward 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 v5.40.1                                       2025-06-26             Mail::SpamAssas...oLearnThreshold(3pm)