https://issues.apache.org/SpamAssassin/show_bug.cgi?id=6344
--- Comment #9 from RW <[email protected]> --- (In reply to comment #8) > If this is a discussion on the efficacy and scoring of RP, DNSWL or other > rules, sobeit. But a discussion of not autolearning specific rules, that > sounds flawed and unmaintainable to me. Here's my thoughts: > > First, to my understanding, the noautolearn setting in question is a > masscheck setting. It doesn't change production systems. No, autolearning uses a non-Bayes score set and additionally ignores rules marked as noautolearn or userconf. > Second, It would seem to me that if you don't trust the set of rules to > score very high, you change the scores. The scores are assigned to distinguish spam from what is not proven to be spam. > Third, If you think the scores are not accurate, we get more people > assisting with rule QA and improve the scores. That works for spam because we optimize for a threshold and then add a safety margin. It wont work for ham because we don't have a three-way classification. Even if we did have a three-way classifiction, we don't have enough "nice" rules to positively identify ham. > Finally, the concept of not learning for the bayesian system based on > certain rules hitting/not-hitting for production systems seems to have > little merit to me. It's more the DNS whitelist rules that are the anomaly. If I add an authenticated address to a whitelist it's ignored for autolearning, but if a direct marketer pays money to Return-Path that does contribute. The DNS whitelists should be seem as a way of avoiding FPs, not as a way of positively identifying ham. -- You are receiving this mail because: You are the assignee for the bug.
