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Old 02-20-2013, 12:09 PM
hemphill hemphill is offline
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Join Date: Jan 2013
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Default Interaction of cross validation with model selection

With 10-fold cross validation, we do 10 training runs. How would you recommend we do model selection? If we have M models, do we do 10*M training runs? Or do we do model selection with simple validation, then use cross validation for an error estimate? There seem to be a lot of possibilities. If we wish to use validation to choose an "early stopping" parameter, we could estimate the value of this parameter in each of the cross validation runs, then use the average when training with the full data set. Is this OK?
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