SVM has numerous variations available to classify high-dimensional data, known as “kernels,” including linear SVC (seen in Figure 12), polynomial SVC, and the Kernel Trick. The Kernel Trick is an advanced solution to map data from a low-dimensional to a high-dimensional space.
