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 used in algorithms like SVM to transform data into higher dimensions. 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 if you expect polynomial relationships.

What are the computational costs of these kernels?

RBF is generally more computationally intensive than linear, while polynomial can vary based on the degree. It’s important to consider your dataset size.

Are there any other kernel functions I should know about?

Yes, there are others like sigmoid and custom kernels. Each has its own use cases depending on the data characteristics.