How does the Kernel Trick work in SVM?

The kernel trick allows SVMs to operate in higher-dimensional spaces without explicit transformation.

Instead of computing explicit feature mappings, kernels compute dot products in transformed feature spaces implicitly. This enables SVMs to separate non-linearly separable data using functions like RBF, polynomial, and sigmoid kernels.