What feature is characteristic of support vector machines in their design?

Support vector machines (SVMs) are fundamentally designed around the concept of creating a hyperplane that can best separate different classes in the feature space. This hyperplane is positioned in such a way that it maximizes the margin, which is the distance between the hyperplane and the nearest points of each class, known as support vectors. The goal is to find the hyperplane that not only separates the classes but does so with the largest possible margin, thereby improving the model's generalization to unseen data.

The ability to effectively separate classes using hyperplanes makes SVMs particularly powerful, especially when dealing with high-dimensional data. In cases where classes are not linearly separable, SVMs can utilize kernel functions to transform the data into a higher-dimensional space where a linear hyperplane can be used for separation. This design feature is a core aspect of how SVMs function and distinguishes them as a robust classification method in machine learning.