Publication date: 10/01/2019

Early Stopping

Early Stopping adds an early stopping rule:

For Forward Selection, the algorithm terminates when 10 consecutive steps of adding variables to the model fail to improve upon the validation measure. The solution is the model at the step that precedes the 10 consecutive steps.

For Lasso, Elastic Net, and Ridge, the algorithm terminates when 10 consecutive values of the tuning parameter fail to improve upon the best fit as determined by the validation method. The solution is the estimate corresponding to the tuning parameter value that precedes the 10 consecutive values.

Note: For the AICc and BIC validation methods, early stopping does not occur until at least four predictors have entered the model.

Go

When you click Go, a report opens. The title of the report specifies the response distribution, the estimation method, and the validation method that you selected. You can return to the Model Launch control panel to perform additional analyses and choose other response distributions, estimation methods, and validation methods.

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