Question

[Thomas Anderson]

Try increasing your robs.  Mine is 0.22.

Other than that, you just have to train on errors.  Your false negatives 
should decrease with training.  Try training til exhaustion, i.e. train 
the same email repeatedly until it classifies correctly.  This should 
prevent you from having to see the same email from many sources before 
it classifies correctly.

Tom

Stephen Davies wrote:
> I understand.
> 
> My initial issue is with the obvious spams not being detected first time 
> round.
> The first I see of them is in my inbox as ham - despite being so obviously 
> spam.
> 
> If I save the email and run it through bogofilter -vvv, I get the results I 
> posted.
> 
> I then use bogofilter -Ns to "fix" the database and this seems to work - until 
> the next spam with the same pattern but from a different source arrives.
> (bogofilter -vvv at this stage gives bogosity of 1.0).
> 
> I have changed my min-dev, robx and robs to 0.35, 0.7, 0.1 but first 
> indications are that this is not enough.
> 
> On Thursday 21 May 2009 10:56:51 RW wrote:
>> On Thu, 21 May 2009 09:49:48 +0930
>>
>> Stephen Davies <scldad at sdc.com.au> wrote:
>>> On Thursday 21 May 2009 06:33:00 Thomas Anderson wrote:
>>>> You have to adjust your robx and robs values.  They will determine
>>>> where never-before-seen and rarely-seen tokens get scored.  E.g. if
>>>> you set your robx within your "unsure" zone, new tokens will never
>>>> score as ham or spam.  And with your robs, you can ensure that
>>>> tokens seen only a few times also remain less influential.
>>> Thanks Tom. I found the doco and that looks like what I need.
>> Just to be clear though, these are not "never-before-seen and
>> rarely-seen tokens", they are tokens from spams that have been learned
>> as ham. If you have a setup where you expect high levels of
>> miss-training, then tuning Bogofilter to mitigate this is sensible -
>> otherwise I'd want to know why it's happening.
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> 
> 
>