False 'false negatives'

Over the past six weeks or so I have been getting between 100 and 200 junk mails in my iCloud junk folder every day. I checked the stats and here they are:

Filtered Mail
72,291 Good Messages
6,247 Spam Messages (8%)
2 Spam Messages Per Day

SpamSieve Accuracy
934 False Positives
89 False Negatives (9%)
98.7% Correct

4,335 Good Messages
5,909 Spam Messages (58%)
1,074,560 Total Words

4,091 Blocklist Rules
18,780 Whitelist Rules

Showing Statistics Since
9/4/15, 9:08 PM

Note: My filter settings are exactly as they are shown in the manual.

My questions: first, is it SpamSieve that is putting the messages it considers to be spam into the junk folder? And if that’s the case, is there a way to get SpamSieve to send junk mail to the trash on receipt?

Second, Looking at the false negatives list, something like three out of four of them are in fact spam. So how do I correct for this?

Third, if this primarily an Apple Mail issue? If so, what email program do people use with more success at blocking spam? I’m filled with dread at the idea of changing, but this piling of of spam is driving me nuts.

Thanks for any help you can provide.

The standard setup has SpamSieve move the messages to Junk. Messages can also go to Junk for other reasons, such as the iCloud server junk filter. This page explains how to tell which messages are which.

Yes, you can change the SpamSieve rule to pick a different destination mailbox.

I’m confused about what you are asking. Your post title and the text here refer to “false negatives,” but then you bolded “false positives” in the statistics above. And you say that 3/4 “are in fact spam,” but all false negatives should be spam. A false negative is an uncaught spam message, i.e. that went to your Inbox instead of Junk.

If you are finding that 1/4 of the false negative files saved to disk are not spam, that means that SpamSieve correctly thought the messages were good but then you erroneously trained them as spam.

Could you clarify what problem you are wanting to solve?

I was waiting for notification that someone had posted an answer, but never got one. I just checked now, so I apologize for the late reply.

Also, I see that I was unclear. To clarify: under 'Accuracy
it reads that there were 89 false negatives, which I understand means that SpamSieve identified those messages as good rather than spam.

You’re right that I had things backwards. I get between 50 and 150 spam emails every day, about 3/4 of them have been trained as ‘spam’ but show up anyway.

Thank you for your patience in pointing this out. And now, is there any way to get a bigger percentage of spam messages interpreted as spam? I routinely ‘train’ messages as good or spam, and have done that for at least a year.

What do the statistics look like if you set the date to a month or two ago? Because the overall stats since 2015 show only 2 spam messages per day. Either the stats are wrong (damaged database file) or a lot has changed.

If you are training 38+ messages as spam per day but there are only 89 missed spams in total since 2015, something is messed up. Either the stats database is damaged or the 38 messages were not actually SpamSieve mistakes. Did you check the coloring and/or log to see whether these were messages that SpamSieve had predicted to be good?

If you need help interpreting this, you could send in a diagnostic report.

I will send you the diagnostic report by email. Thanks for your help.

Thanks for sending the diagnostic report. It looks like the reason SpamSieve hasn’t been catching any of these is that it’s not enabled in Mail. Please make sure that you have a SpamSieve rule set up in Mail’s preferences, as shown in Step 3.

Michael thank you for looking at the report. At first I was embarrassed to have missed something so obvious, but in fact SpamSieve was enabled as described in the instructions. I’m attaching screen shots. Does this mean I’m back at square one?

You screenshots are showing the plug-in in Step 2 and Full Disk Access in Step 1. The part that’s missing is the rule in Step 3.