# feature importance for SVM with a nonlinear kernel

Using sklearn, I did SVR using rbf kernel. Though I got good results, problem is I don't know how to get the important feature that the algorithm used.

Also coef_ only works for linear kernel, since for non linear kernel data space is not finite.

Is there any way to get features importance?