Note: Case is n>>p
I am reading Elements of Statistical Learning and there are various mentions about the "right" way to do cross validation( e.g. page 60, page 245). Specifically, my question is how to evaluate the final model (without a separate test set) using k-fold CV or bootstrapping when there has been a model search? It seems that in most cases (ML algorithms without embedded feature selection) there will be
- A feature selection step
- A meta parameter selection step (e.g. the cost parameter in SVM).
- I have seen that the feature selection step can be done where feature selection is done on the whole training set and held aside. Then, using k-fold CV, the feature selection algorithm is used in each fold (getting different features possibly chosen each time) and the error averaged. Then, you would use the features chosen using all the data (that were set aside) to train the final mode, but use the error from the cross validation as an estimate of future performance of the model. IS THIS CORRECT?
- When you are using cross validation to select model parameters, then how to estimate model performance afterwards? IS IT THE SAME PROCESS AS #1 ABOVE OR SHOULD YOU USE NESTED CV LIKE SHOWN ON PAGE 54 (pdf) OR SOMETHING ELSE?
- When you are doing both steps (feature and parameter setting).....then what do you do? complex nested loops?
- If you have a separate hold out sample, does the concern go away and you can use cross validation to select features and parameters (without worry since your performance estimate will come from a hold out set)?
Many thanks to anyone who can clear this up! Sorry for a wordy question but I wanted to make sure I explained the confusion points (many ;-)) Brian