Early Stopping

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Fitting Linear Models • Generalized Regression Models • Model Launch Control Panel • Early Stopping

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Early Stopping

Early Stopping adds an early stopping rule:

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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.

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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.

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Help created on 7/12/2018