When training a model a train, a validation and test set are used. I was wondering if there is any paper or example that proves that the use of an independent validation set increase the performance of the lasso estimator. I am particularly interested in situations where the penalty value is chosen through cross validation
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The independent validation is not supposed to increase the performance: it is supposed to measure the performance of the final model (and detect/monitor the optimistic bias introduced to model selection during data-driven optimization such as cross validating for the "optimal" penalty).
answered May 31, 2014 at 11:21
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$\begingroup$ thanks for answer can you write an example ? $\endgroup$– DonbeoCommented May 31, 2014 at 13:56
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1$\begingroup$ I can, but I won't: there are already many questions and answers on the problematic of data-driven model optimization/selection available (stats.stackexchange.com/search?q=data-driven+model+optimization). Here are starting points for further reading: stats.stackexchange.com/questions/79905/… stats.stackexchange.com/questions/5918/… $\endgroup$ Commented May 31, 2014 at 14:04