How do support vector machines work?

Can you explain how support vector machines work?

Sure! Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks. They work by finding the hyperplane that best separates different classes in the feature space.

What do you mean by hyperplane?

A hyperplane is a flat affine subspace of one dimension less than its ambient space. In SVM, it acts as a decision boundary that separates different classes.

How does SVM handle non-linear data?

SVM can handle non-linear data by using kernel functions, which transform the data into a higher-dimensional space where a linear separator can be found.

What are some common kernel functions used in SVM?

Common kernel functions include linear, polynomial, and radial basis function (RBF) kernels. Each has its own advantages depending on the data.