SpamAssassin Rule: BAYES_95
Standard description: Bayes spam probability is 95 to 99%
SpamAssassin includes a Bayesian filter that assigns scores based on the user's previous email history. This can assign both positive and negative scores. For instance, a user may receive a particular spam message several times via a relay identified in a DNSBL, so that SpamAssassin correctly identifies it as spam. If the user receives the same message via a new unlisted relay, the Bayesian algorithm will assign a high score to it based on previous experience.
Conversely, if a user receives a regular newsletter from a fitness club, and one issue makes reference to diet pills and weight loss (which would normally flage the message as spam), the Bayesian algorithm will assign a lower score to it.
The default scores for this rule can be found in the online list of tests.