| Publisher | SAS Institute | ||
|---|---|---|---|
| Format | 138.3KB PDF | Date added | 09 Mar 2005 |
| Topics | Knowledge and Data Management, Data Mining - Analysis | ||
| Downloads | 90 | ||
The GSForward node is an implementation of the methods described in Foster and Stine (2004), who present a novel approach in variable selection with a large number of variables. Foster and Stine provide a method to select a very good predictive model for a binary dependent target out of more than 67,000 predictors. They arrive at this large number of predictors by creating all product interactions of the original centered 200 or so variables, to which missing value indicators are also added. Variable centering (mean removal) ameliorates but does not eliminate colinearity arising from variable products. The missing values themselves are mean imputed.
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