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The scores are assigned using a neural network trained with error back propagation (\["Perceptron"\]). Both systems attempt to optimiseoptimize the efficiency of the rules that are run in terms of minimizing the number of false positives and false negatives. |
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You can help this system by providing statistics on your mail spool via NightlyMassCheck and RescoreMassCheck.
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Confusing
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scores
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Scores for "learn" rules (example the various BAYES_?? rules) are scored using the same method. This can produce scores which seem incorrect (example BAYES_80 with a higher score than BAYES_99). This is due to the fact that rules are not related to one another, they're separate rules have separate scores.
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A score of 0 will stop a rule from being run.
In version 2.x, the scores are assigned using a genetic algorithm (GA).