Well the numbers I am seeing are totally unreliable anyway (for
blocking spam at least).  I see valid emails at 1.0 and spam at .99 or
that doesn't get hit at all.

I was just wondering the meanings and maybe trying to dive a little
deeper into their methodology.

SpamAssassin seems to be much more efficient as far as giving email a
ranking.

On Tue, 03 Jun 2003 15:31:15 -0400, you wrote:

>
>> > Specifically, what the numbers mean.  I am seeing .9xxx and 1.000 in the
>> > header.  Does this mean 97% chance it is spam and 1.00 is a 100% chance it
>> > is spam?
>>
>> > X-IMAIL-SPAM-STATISTICS: 0.9787
>
>>You are correct.
>
>Actually (Ipswitch can correct me if I am wrong), this is not a true 
>probability but a "pseudo-probability" (a number between 0 and 1 that acts 
>like a probability, but isn't one).  With naive Bayes theorem, for example, 
>you will often get E-mails that show a 99.9999% chance of being spam 
>(that's 1 in 1 million chance), whereas the real probability might really 
>be something like 99.8% chance (1 in 500 chance).  Naive Bayes Theorem 
>essentially simulates extremely complex calculations (too complex to be 
>done in real-time), and ends up coming up with estimates that are normally 
>very close numerically to the actual probabilities.
>
>In some situation, exact probabilities are much better than estimates.  For 
>example, with web hosting, the difference between 99.5% uptime and 99.99% 
>uptime is drastic.  However, for spam control, the difference between 99.5% 
>and 99.99% isn't very important (since many people would delete the E-mail 
>in either case).  In this case, estimates are much more acceptable.
>
>The key here is not to run any true statistical calculations based on the 
>number you see (for example, don't assume that if you see 1.0000, that you 
>will have a 0% false positive ratio, or that with .9990 you'll have 30 
>false positives a month if you process 1,000 E-mails/day).
>
>                                                    -Scott
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