Am 21.09.2016 um 18:47 schrieb Bowie Bailey:

That is ridiculous.  The more training bayes gets the better it works.
And manual training is better than autolearning because autolearning can
automatically learn false positives and false negatives and cause
problems for the database.

And what about filter poisening? In the last 10 hours my company address got 43 mails classified as spam (even a virus mail detected today). And there was one mail classified as spam due to my rule (bad country, message-id.

X-Spam-Status: Yes, score=7.474 tag=2 tag2=6.31 kill=6.31
        autolearn=no autolearn_force=no

The content of the mail is:

From: "Lupe Monroe" <>
To: "my boss address"
Subject: Payment approved
MIME-Version: 1.0
Content-Type: multipart/related;
Message-Id: <>
Date: Thu, 22 Sep 2016 06:32:55 +0700

Content-Type: text/plain; charset="utf-8"
Content-Transfer-Encoding: 8bit

Dear so,

Your payment has been approved. Your account will be debited within two days.

You can email us for any query regarding your account.

Thank you.

Lupe Monroe

Content-Type: application/x-zip-compressed; name=""
Content-Transfer-Encoding: base64
Content-Disposition: attachment; filename=""

There is no spam content, am I right? Normal words and content that a normal person can use. I dont need spam learning for all the mails already classified as spam with high score. Spam with low score are interesting for spam learning like this one. But when I use these mails for spam learning there is a risk of false positive some day, because it has learned that normal mails are also spam?

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