• Estimation Method Options
 • Validation Method Options
 • Early Stopping
 • Go
Estimation Method Options
 • the l1 penalty, which penalizes the sum of the absolute values of the regression coefficients
 • the l2 penalty, which penalizes the sum of the squares of the regression coefficients
Computes parameter estimates using ridge regression. Ridge regression is a biased regression technique that applies an l2 penalty and does not result in zero parameter estimates. It is useful when you want to retain all predictors in your model. For more details, see Ridge Regression.
Computes parameter estimates by applying an l1 penalty. Due to the l1 penalty, some coefficients can be estimated as zero. Thus, variable selection is performed as part of the fitting procedure. In the ordinary Lasso, all coefficients are equally penalized.
Computes parameter estimates by penalizing a weighted sum of the absolute values of the regression coefficients. The weights in the l1 penalty are determined by the data in such as way as to guarantee the oracle property (Zou, 2006). This option uses the MLEs to weight the l1 penalty. MLEs cannot be computed when the number of predictors exceeds the number of observations or when there are strict linear dependencies among the predictors. If MLEs for the regression parameters cannot be computed, a generalized inverse solution or a ridge solution is used for the l1 penalty weights. See Adaptive Methods.
Computes parameter estimates by applying both an l1 penalty and an l2 penalty. The l1 penalty ensures that variable selection is performed. The l2 penalty improves predictive ability by shrinking the coefficients as ridge does.
Computes parameter estimates using an adaptive l1 penalty as well as an l2 penalty. This option uses the MLEs to weight the l1 penalty. MLEs cannot be computed when the number of predictors exceeds the number of observations or when there are strict linear dependencies among the predictors. If MLEs for the regression parameters cannot be computed, a generalized inverse solution or a ridge solution is used for the l1 penalty weights. You can set a value for the Elastic Net Alpha in the Advanced Controls panel. See Adaptive Methods.
 • When the tuning parameter is too small, the data are typically overfit and result in models with high variance.
 • When the tuning parameter is too large, the data are typically underfit.
 • For the Lasso, Elastic Net with Elastic Net Alpha specified, and Ridge, the value of the tuning parameter that gives the solution is the one that provides the best fit over the grid of tuning parameters.
 • If the design matrix is singular, the maximum likelihood estimates cannot be computed. The lower bound of 0.01 allows estimates close to the MLEs to be computed.
 • If the selected distribution is binomial, the lower bound of 0.01 helps prevent separation.
Requires lower-order effects to enter the model before their related higher order effects. In most cases, this means that X2 is not in the model unless X is in the model. For estimation methods other than Forward Selection, however, it is possible for X2 to enter the model and X to leave the model in the same step. If the data table contains a DOE script, this option is enabled, but it is off by default.
Sets the α parameter for the Elastic Net. This α parameter determines the mix of the l1 and l2  penalty tuning parameters in estimating the Elastic Net coefficients. The default value is α = 0.9, which sets the coefficient on the l1 penalty to 0.9 and the coefficient on the l2 penalty to 0.1. This option is available only when Elastic Net is selected as the Estimation Method. See Statistical Details for Estimation Methods.
Provides options for choosing the solution that is initially displayed as the current model in the Solution Path report. The current model is identified by a solid vertical line. See Current Model Indicator. The best fit solution is identified by a dotted vertical line. By default, the displayed solution is the one that is considered the best fit according to the specified Validation Method.
You can choose to initially display models with larger or smaller l1 norm values that still lie in the green or yellow zones. For example, if you choose Smallest in Yellow Zone, the initially displayed solution is the model in the yellow zone that has the smallest l1 norm. See Comparable Model Zones.
Validation Method Options
 – The observations are partitioned into k subsets, or folds.
 – In turn, each fold is used as a validation set. A model is fit to the observations not in the fold. The log-likelihood based on that model is calculated for the observations in the fold, providing a validation log-likelihood.
 – The mean of the validation log-likelihoods for the k folds is calculated. This value serves as a validation log-likelihood for the value of the tuning parameter.
The value of the tuning parameter that has the maximum validation log-likelihood is used to construct the final solution. To obtain the final model, all k models derived for the optimal value of the tuning parameter are fit to the entire data set. Of these, the model that has the highest validation log-likelihood is selected as the final model. The training set used for that final model is designated as the Training set and the holdout fold for that model is the Validation set. These are the Training and Validation sets used in plots and in the reported results for the final solution.
Minimizes the Extended Regularized Information Criterion (ERIC) over the solution path. See Model Fit Detail. Available only for exponential family distributions and for the Lasso and adaptive Lasso estimation methods.
Early Stopping
Go
When you click Go, a report opens. The title of the report specifies the fitting and validation methods that you selected. You can return to the Model Launch control panel to perform additional analyses and choose other estimation and validation methods.

Help created on 9/19/2017