Wednesday, October 01, 2008
i found another question. the RMSE is always increasing as the ridge constant goes larger(i.e. more restrict on beta's), as is shown in SAS. Here RMSE is generated from all observations, not some certain folds.
but i guess the result is the same if we get RMSE by Cross-Validation on some folds, cuz after trying many lambda's, i found that the best lambda is always the one before last candidate bounds, using the code you give. acutually the cvRMSE is always decreasing as bound goes to 1. this is for sure: if you put more constraints on beta's, you lose some accuracy on prediction.
so the question is: how do we find the BEST ridge if the cvRMSE is monotonely increasing as ridge constant goes larger?
thank you!
When I run l1ce with the bank data, cvRMSE selects a lambda that
is not the largest...
but i guess the result is the same if we get RMSE by Cross-Validation on some folds, cuz after trying many lambda's, i found that the best lambda is always the one before last candidate bounds, using the code you give. acutually the cvRMSE is always decreasing as bound goes to 1. this is for sure: if you put more constraints on beta's, you lose some accuracy on prediction.
so the question is: how do we find the BEST ridge if the cvRMSE is monotonely increasing as ridge constant goes larger?
thank you!
When I run l1ce with the bank data, cvRMSE selects a lambda that
is not the largest...
