What are the differences between various kernel functions in machine learning?

What are the differences between various kernel functions in machine learning?

Kernel functions are crucial in algorithms like SVM. Common types include linear, polynomial, and RBF.

Can you explain how the RBF kernel works?

The RBF kernel, or Gaussian kernel, measures similarity based on the distance between points, allowing for non-linear decision boundaries.

When should I use a polynomial kernel instead of RBF?

Use a polynomial kernel when you want to capture interactions between features, especially in lower-dimensional spaces.

Are there any drawbacks to using the RBF kernel?

Yes, the RBF kernel can be sensitive to the choice of the bandwidth parameter, which can lead to overfitting if not tuned properly.