examples I have the actual class labels (binary). The data is quite
unbalanced with about 10% or less of the examples having a positive outcome
and the remainder, of course, negative. Nothing suggests the data has any
order, and it doesn't appear to have any, so I've pulled the first 30,000
examples to use as training data, reserving the remainder for test data.
There are actually 3 distinct sets of class labels associated with the
predictor data, and I've built 3 distinct models. When each model is used in
predict() with the training data and true class labels, I get AUC values of
0.95, 0.98 and 0.98 for the 3 classifier problems.
When I run these models against the 'unknown' inputs that I held out--the
20,000 instances--I get AUC values of about 0.55 or so for each of the three
problems, give or take. I reran the entire experiment, but instead using
40,000 instances for the model building, and the remaining 10,000 for
testing. The AUC values showed a modest improvement, but still under 0.60.
I've looked at a) the number of unique values that each predictor takes on,
and b) the number of values, for a given predictor, that appear in the test
data that do not appear in the training data. I can eliminate variables
that have very few non-null values, and those that have very few unique
values (the two are largely the same), but I wouldn't expect this to have
any influence on the model.
I've already eliminated variables that are null in every instance, and
duplicate variables having identical values for all instances. I have not
done anything to check further for dependant variables, and don't know how
to.
Besides getting a clue, what might be my next best step?
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