I'm running spamc/spamd 3.0.2 in Debian. I have Bayesian tests turned on, and network tests off.
Lately a lot of spam has been getting through to my mailbox. SA's false negative rate used to be about 1%; now it's about 50%. Looking at the headers for the spam that's getting through, I see that the Bayesian filter is working correctly: almost all of the spam is tagged as BAYES_95 or BAYES_99. My score threshold is 5, the BAYES_99 test alone (using its default value) is worth 4.07, and a few other tests are usually positive as well. Yet, the total score is around 2.5. Here's a sample from today: X-Spam-Status: No, score=2.7 required=5.0 tests=BAYES_99,HTML_20_30, HTML_FONT_INVISIBLE,HTML_IMAGE_ONLY_24,HTML_MESSAGE autolearn=no version=3.0.2 The scores from the tests listed here should add up to about 5.3, but as you can see, the total is only 2.7. So this one gets through. I understand that the individual test scores are fed through a neural network to derive the final score. So it seems that this network has started to behave badly. Can anyone shed any light on this? Is it a well-known problem? What's the preferred way to address it? Remove all of SA's learned information and retrain the network? Thanks, Andrew.
