Validation Method Options
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.

Help created on 7/12/2018