# Model selection (GLE models)

Use model selection or regularization. To activate the controls on this pane, select this check box.

Method. Select the method of model selection or (if
using Ridge) the regularization to be used. You can choose from the following
options:

- Lasso. Also known as L1 Regularization, this method is faster than Forward Stepwise if there are a large number of predictors. This method prevents overfitting by shrinking (that is, by imposing a penalty) on the parameters. It can shrink some parameters to zero, performing a variable selection lasso.
- Ridge. Also known as L2 Regularization, this method prevents overfitting by shrinking (that is, by imposing a penalty) on the parameters. It shrinks all the parameters by the same proportions but eliminates none and is not a variable selection method.
- Elastic Net. Also known as L1 + L2 Regularization, this method prevents overfitting by shrinking (that is, by imposing a penalty) on the parameters. It can shrink some parameters to zero, performing variable selection.
- Forward Stepwise. This method starts with no effects in the model and adds or removes effects one step at a time until no more can be added or removed, according to the stepwise criteria.

Automatically detect two-way interactions. To automatically detect two-way interactions, select this option. Note that GLE only detects the interaction of two categorical variables and squared of continuous variables. It doesn't detect the interaction of two continuous variables and the interaction of categorical and numerical variables.