https://issues.apache.org/SpamAssassin/show_bug.cgi?id=6344

--- Comment #13 from RW <[email protected]> ---
(In reply to comment #12)

>  I continually keep circling back to the fact that
> rules should score appropriately with minimal false hits.  That includes
> hammy rules.

As I said before, we don't have any meaningful QA mechanism for this.

It's not possible to optimize for two thing simultaneously. The score-set that
optimizes the TP rate at 5.0 with an FP constraint, isn't going to be an
optimal score-set for maximizing ham learning at 0.1 with a mislearning
constraint.

In theory it is possible to do it with a single optimization if you close the
loop and allow mistraining to affect the scores at 5.0, but that means that all
the BAYES results would need to be dynamically recomputed from a fresh database
for each set of rule scores, and that's simply impractical.

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