I have a few related questions:
- What is the total number of fitted paramaeters in Python Support Vector Machine: sklearn.svm.SVC(kernel='linear') and sklearn.svm.SVC(kernel='rbf')?
I am trying to find out the total number of fitted parameters in linear and kernel SVM. If I understand correctly, the fitted parameters include C (penalty parameter), gamma(for kernel='rbf', none for kernel='linear'), all the coefficients (for kernel = 'linear', equal to the number of features + 1 for the intercept; for kernel = 'rbf', equal to the number of training samples), and slack variables (equal to the number of samples) - is this correct?
- If the above is correct, does it imply that there is a high probability of overfitting for SVM with kernel = 'rbf' since the number of fitted parameters is always greater than the number of samples?
Thank you very much for your help!