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In the GLE modeling node, include support for cross validation in conjunction with regularization.
When using regularization methods like Lasso or Ridge, it would be helpful to be able to select the optimal penalty parameter based on k-fold cross validation with a user-selectable option for k. Model output should include chart of cross validation results to guide the user in deciding what regularization parameter to use in scoring.
Can currently circumvent this shortcoming by using an R model node with calls to functions from the R package 'glmnet'.
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