Generalized Regression Overview
The Generalized Regression personality features regularized, or penalized, regression techniques. Such techniques attempt to fit better models by shrinking the model coefficients toward zero. The resulting estimates are biased. This increase in bias can result in decreased prediction variance, thus lowering overall prediction error. Two of these techniques, the Elastic Net and the Lasso, include variable selection as part of the modeling procedure.
Modeling techniques such as the Elastic Net and the Lasso are particularly promising for large data sets, where collinearity is typically a problem. In fact, modern data sets often include more variables than observations. This situation is sometimes referred to as ...

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