What is found by support vector machines to separate different classes in data?

Support vector machines (SVMs) are supervised learning models used for classification and regression tasks. The primary mechanism by which SVMs achieve classification is through the concept of a hyperplane. In a multi-dimensional space defined by the features of the data, a hyperplane serves as a decision boundary that separates different classes.

The goal of the support vector machine is to identify the hyperplane that maximizes the margin between the classes. This margin is defined as the distance between the hyperplane and the closest data points from either class, known as support vectors. By maximizing this margin, SVMs enhance the model's ability to generalize to new data, reducing the likelihood of overfitting.

In summary, the hyperplane is fundamental in SVMs for delineating how different classes can be separated effectively in the feature space, which is critical for successful classification in many practical applications.