What are support vector machines used for?

Support vector machines (SVM) are a type of supervised learning algorithm primarily used for classification and regression tasks. In supervised learning, the model is trained on a labeled dataset, which means that each training example is paired with a corresponding target label. The objective of SVMs is to find the optimal hyperplane that separates different classes in the feature space. This hyperplane is determined by the support vectors, which are the data points closest to the decision boundary and have the greatest influence on its positioning.

The recognition of SVMs as a supervised learning technique is crucial because it differentiates them from unsupervised learning models, which deal with data without pre-existing labels. While some models in machine learning perform tasks like clustering or association—characteristics of unsupervised learning—SVMs rely on labeled data to succeed in their classification tasks. This distinction makes option B the accurate choice among the alternatives provided.