What are the different types of kernels in SVM?

What are the different types of kernels used in Support Vector Machines?

In SVM, the most common types of kernels are linear, polynomial, radial basis function (RBF), and sigmoid.

Can you explain how the RBF kernel works?

The RBF kernel maps input features into a higher-dimensional space, allowing for non-linear decision boundaries.

What are the advantages of using a polynomial kernel?

The polynomial kernel can model complex relationships and is useful for datasets where the relationship is not linear.

Are there any drawbacks to using the sigmoid kernel?

Yes, the sigmoid kernel can be less effective in practice and may not perform well compared to other kernels.