AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

The margin in Support Vector Machines (SVM) is defined as the maximum distance between the decision boundary (often referred to as the hyperplane) and the closest data points from each class. These closest points are known as support vectors. The purpose of maximizing this margin is to create a robust classifier that not only separates the data into different classes but also ensures that the separation is as wide as possible, which can enhance the generalization capabilities of the model on unseen data.

By focusing on the maximum separation distance between classes, SVM aims to find a decision boundary that is most likely to maintain its accuracy when making predictions. A larger margin typically implies lower generalization error, thus making the SVM effective for classification tasks. This concept is fundamental to how SVMs operate, as they prioritize finding a hyperplane that best separates the classes while maximizing this margin, ultimately improving prediction stability and performance.