# SVM kernel parameter and tunning parameter

In the svm function, you can apply three cases to the kernel parameter. "Linear," "radial," and "polynomia."

And I try to derive the optimal svm result by adjusting cost, gamma and degree parameters.

What values should be used in each kernel?

1. linear kernel :
3. polynomia kernel :

What parameter do i have to use in each kernel????????

• The software documentation is a great place to look for information about how to use software. – Sycorax Jun 22 '18 at 14:34

The reason using the default value of the gamma parameter in the radial kernel is that because of the mathematical formulation of the radial kernel you can usually get similar results by adjusting just the cost. The default gamma value for sklearn SVM for example is $\frac{1}{n_{features}}$.