AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Kernelling in Support Vector Machines (SVM) refers to the process of mapping data into a higher-dimensional space. This technique allows SVM to handle non-linear relationships by transforming the original input features into a new set of features where a linear separation is easier to achieve. Essentially, it expands the feature space, enabling the SVM algorithm to create a hyperplane in this higher-dimensional space that can effectively separate different classes.

The key aspect of the kernel trick is that it allows the computation of the necessary operations in this higher-dimensional space without explicitly having to perform the transformation of the original data points. Instead, kernels compute the dot product of the images of the data points in the higher-dimensional space directly, which is often more computationally efficient.

Mapping data into a higher dimensional space is crucial for SVMs to achieve effective classification, especially when the original space is not linearly separable. This is why the provided answer aligns with the fundamental concept of kernelling in SVM.