Kernel Trick

The kernel trick transforms complex, non-linear problems into simpler ones by mapping data into a higher-dimensional space without explicitly computing the transformation. This allows SVMs to find linear separators in the new space.

Common kernels include polynomial, RBF, and sigmoid. The trick enables efficient handling of non-linearly separable data with minimal computational overhead.