Hi,
I have a strange one for the group.
We have a system that predicts probabilities using a fairly standard svm
(e1017). We are looking at probabilities of a binary outcome.
The input data is generated by a perl script that calculates a bunch of
things, fetches data from a database, etc.
Predicting whilst confused is unlikely to produce sound predictions...
my vote is for finding out why before believing anything.
Noah Silverman n...@smartmediacorp.com 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
On Mon, Sep 7, 2009 at 12:33 PM, Noah Silvermann...@smartmediacorp.com wrote:
SNIP
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
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 1:22 PM, Noah Silvermann...@smartmediacorp.com wrote:
SNIP
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 -
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
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