Lots of spam gets through because of BAYES_00 -2.60

[Greg Matthews]

Gareth wrote:
> Personally I find that it is very difficult to make bayes particularly 
> effective in a corporate enviroment because of the variety of mails 

this is not a reflection on the usefulness of Bayes. Proper 
configuration will make this an extremely useful part of the anti-spam 
suite.

> people receive. Therefore I find the low scoring bayes rules give a far 
> to big a negative score.  I tend to overise the low and high scores with 
> the following :-
>  
> score BAYES_00 -0.5
> score BAYES_05 -0.1
> score BAYES_20 -0.01
> score BAYES_40 -0.01
> score BAYES_99  5.0
> 

interesting, your high-end scores aren't as conservative as your low 
end. I wonder if you are managing to auto-learn enough ham? You know you 
can adjust the autolearn thresholds dont you? Its quite common for Bayes 
to have far more spam to learn from than ham which without attention 
results in having to skew the scores as you have above.

Personally, I have great success with Bayes on relays that filter around 
20-30k messages per day across 20-30 domains and around 5000 mailboxes. 
I am careful tho to feed back all false postives flagged up by users 
(perhaps as many as 5 per week) back into the system. I also feed back 
all my own (personal) false negatives which may be as many as 10 per 
week (<1% of my mail).

In summary, if Bayes is not working for you, its worth taking the time 
to get it right rather than simply skewing the scores.

-- 
Greg Matthews           01491 692445
Head of UNIX/Linux, iTSS Wallingford

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