Body: <p>I have been recently reading about model selection algorithms (for example to decide which value of the regularisation parameter or what size of a neural network to use, broadly hyper-parameters). This is done by dividing the examples into three sets (training 60%, cross-validation 20%, test 20%) and training is done on the data with the first set for all parameters, and then choose the best parameter based on the result in the cross-validation and finally estimate the performance using the test set.</p>
  
  <p>I understand the need for a different data-set compared to training and test for select the model, however, once the model is selected, why not using the cross-validation examples to improve the hypothesis before estimating the performance?</p>
  
  <p>The only reason I could see is that this could cause the hypothesis to worsen and we wouldn't be able to detect it, but, is it really possible that by adding much more examples (60% -> 80%) the hypothesis gets worse?</p>
  
Tags: <neural-networks><machine-learning><training><datasets>
Title: Use cross-validation to train after model selection
UserDisplayName: gcorso
ThreadText: # Use cross-validation to train after model selection
  
  (Asked by: username_0 on 2018-08-29)
  
  I have been recently reading about model selection algorithms (for example to decide which value of the regularisation parameter or what size of a neural network to use, broadly hyper-parameters). This is done by dividing the examples into three sets (training 60%, cross-validation 20%, test 20%) and training is done on the data with the first set for all parameters, and then choose the best parameter based on the result in the cross-validation and finally estimate the performance using the test set.
  
  I understand the need for a different data-set compared to training and test for select the model, however, once the model is selected, why not using the cross-validation examples to improve the hypothesis before estimating the performance?
  
  The only reason I could see is that this could cause the hypothesis to worsen and we wouldn't be able to detect it, but, is it really possible that by adding much more examples (60% -> 80%) the hypothesis gets worse?
  
  **Tags:** neural-networks, machine-learning, training, datasets
  
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  ## 0 answers
  
  