I was wondering if the features selected with RFE with SVM linear kernel are still "good" features when we use a non linear model, like SVM rbf kernel. This comes in practice when you want to use SVM as a classifier for the RFE but you are forced to stick to linear kernel: maybe you can do the selection with SVM linear and then the prediction with SVM rbf? If I would answer I would say yes: the features selected with linear SVM are able to explain a linear relationship between descriptors and output, so if we use a non linear model they are still useful. If you can give an explation, it will be quite appreciated.


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.