Em Sáb, 2008-09-27 às 10:51 -0700, milicic.marko escreveu:
> I have a huge data set with thousands of variable and one binary
> variable. I know that most of the variables are correlated and are not
> good predictors... but...
>
> It is very hard to start modeling with such a huge dataset. What would
> be your suggestion. How to make a first cut... how to eliminate most
> of the variables but not to ignore potential interactions... for
> example, maybe variable A is not good predictor and variable B is not
> good predictor either, but maybe A and B together are good
> predictor...
>
> Any suggestion is welcomed
milicic.marko
I think do you start with a rpart("binary variable"~.)
This show you a set of variables to start a model and the start set to
curoff for continous variables
--
Bernardo Rangel Tura, M.D,MPH,Ph.D
National Institute of Cardiology
Brazil
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