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.
We train the system on 30,000 examples and then test the system on an
unseen set of 5,000 records.
The "real world" results on the test set looked VERY good. We were
really happy with our model.
The, we noticed that there was a big error in our data generation script
and one of the values (an average of sorts.) was being calculated
incorrectly. (The perl script failed to clear two iterators, so they
both grew with every record.)
As an quick experiment, we removed that item from our data set and
re-ran the process. The results were not very good. Perhaps 75% as
good as training with the "wrong" factor included.
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
predicting more accurately
Any opinions??
Thanks!
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