I am currently exploring two regression methods using kernels, namely Kernel Ridge Regression (KRR) and Support Vector Regression (SVR). I tune their parameters using a randomized grid search.

Using a polynomial kernel, KRR outperforms SVR, but when I use an rbf kernel it is the other way around.

What might be an intuitive explanation for this? Or should I interpret this as a specific trait of the dataset that I am analyzing?



Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.