What are the differences between kernel types in machine learning?

What are the differences between kernel types in machine learning?

In machine learning, kernel types are functions used in algorithms like SVM to transform data into higher dimensions. The main types include linear, polynomial, and radial basis function (RBF) kernels.

Can you explain how the polynomial kernel works?

Sure! The polynomial kernel computes the similarity between two vectors in a feature space of polynomial combinations of the original features, allowing for non-linear decision boundaries.

What are the advantages of using the RBF kernel?

The RBF kernel is effective in handling non-linear relationships and is less prone to overfitting, making it a popular choice for many datasets.

When should I use a linear kernel instead?

A linear kernel is best used when the data is linearly separable, as it is simpler and faster to compute, reducing the risk of overfitting.