Kernelling is a technique where the SVM uses a kernel function to map the dataset into a higher-dimensional space, making it easier to identify separable clusters that may not be apparent in the original low-dimensional space.
Kernel Trick:
- When the data cannot be separated by a straight line or plane in its original (low-dimensional) space, SVM uses a technique called kernelling to project the data into a higher dimension where it becomes easier to separate.
- The Kernel Trick allows the transformation of data into a higher dimension without explicitly computing the transformation. There are different types of kernels, with common examples being:
- Polynomial kernel
- Radial Basis Function (RBF) or exponential kernel