CentOS 7.0 - man page for mail::spamassassin::plugin::autolearnthreshold (centos section 3)
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Mail::SpamAssassin::Plugin::AutoLearnThreshold - threshold-based discriminator for Bayes
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
Note that certain tests are ignored when determining whether a message should be trained
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.
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
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 v5.16.3 2011Mail::SpamAssassin::Plugin::AutoLearnThreshold(3)
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