#1




Multiple validation sets
Hello everybody,
I was listening to Prof. Mostafa’s lecture on validation, and at one point he mentioned that we can have more than one validation set (if our data permits) to make multiple choices on different models and/or parameters. That got me thinking about how one may go about doing that. Say we have just enough data to carve out a training set and two validation sets. We have two decisions to make, one is which order of polynomial to fit the data with (2nd order, 3rd order, or 5th order), and the other is what value of the regularization parameter to use (lambda equals 0.01, 0.1, 1 or 2). What is the best way of doing this? I can think of the following ways, and would appreciate any feedback on which approach, if any, might be the best. 1. We have a total of 12 combinations of choices. So we work out the 12 candidate hypotheses on the training set, and combine the 2 validation sets into one set to choose the best hypothesis with. This may “contaminate” the validation set a bit more than we want, as the number of hypotheses is not small. We then combine the training and the validation set to produce the final hypothesis with the best polynomial order and the best lambda. 2. We use the training data to first produce 3 hypotheses based on polynomial order, with a fixed value of lambda chosen from among its four possible values. We decide the best polynomial order using the first validation set. Then we use the training set again with the just decided polynomial order to produce 4 hypotheses based on the values of lambda. We use the second validation set to decide the best value of lambda. Finally, we combine all the data to come up with the best hypothesis with the chosen polynomial order and the chose lambda value. 3. We do the same as option 2, but reverse the order of deciding  find lambda first, then the polynomial order. 4. We do the same as options 2 or 3, but after the first decision, we combine the training set and the first validation set to produce a second, bigger training set for the second decision. These are the four options I can think of. In option 2, I am not sure if randomly choosing a value of lambda while we are deciding the best polynomial order is wise. Also not sure if using the same training set again and again is OK (wouldn't it get horribly contaminated?) Any thoughts or comments are much appreciated. 
Tags 
generalization, validation 
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