When I receive a notification that SpamSieve is unsure that a message is spam, I have been training the message as spam, thinking that this would make SpamSieve more certain on the next such message.
However, these messages are in the Spam folder, and colored yellow. I have just read a response from a developer that training SpamSieve that yellow messages are spam degrades its ability to distinguish good from spam, so now I’m concerned that I’ve been undermining its accuracy.
a. Should I avoid doing any training of SpamSieve on yellow spam messages?
b. How should I help SpamSieve know that borderline cases (which it has identified as spam) are indeed spam?
c. Should no training be done of messages in the correct folder (spam folder or good folder)?
d. If I’ve been doing this superfluous (and possibly counterproductive) training, should I zero out training to date and start anew, perhaps letting a bunch of real spam build up in the spam folder for an initial training session?