Interesting point.
Our data is NOT continuous. Sure, some of the test examples are older
than others, but there is no relationship between them. (More Markov
like in behavior.)
When creating a specific record, we actually account for this in our SQL
queries which tend to be along the lines of
On Mon, Sep 7, 2009 at 1:22 PM, Noah Silverman wrote:
>
> The data is listed in our CSV file from newest to oldest. We are supposed
> to calculated a valued that is an "average" of some items. We loop through
> some queries to our database and increment two variables - $total_found and
> $total_
You both make good points.
Ideally, it would be nice to know WHY it works.
Without digging into too much verbiage, the system is designed to
predict the outcome of certain events. The "broken" model predicts
outcomes correctly much more frequently than one with the broken data
withheld. So,
You both make good points.
Ideally, it would be nice to know WHY it works.
Without digging into too much verbiage, the system is designed to
predict the outcome of certain events. The "broken" model predicts
outcomes correctly much more frequently than one with the broken data
withheld. So,
On Mon, Sep 7, 2009 at 12:33 PM, Noah Silverman wrote:
> So, this is really a philosophical question. Do we:
> 1) Shrug and say, "who cares", the SVM figured it out and likes that bad
> data item for some inexplicable reason
> 2) Tear into the math and try to figure out WHY the SVM is predi
Predicting whilst confused is unlikely to produce sound predictions...
my vote is for finding out why before believing anything.
>>> Noah Silverman 09/07/09 8:33 PM >>>
Hi,
I have a strange one for the group.
We have a system that predicts probabilities using a fairly standard svm
(e1017). We
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