Which statement is true about support vector machines (SVM)?

Support Vector Machines (SVM) are a powerful supervised learning model used primarily for classification tasks. The correct statement focuses on how SVM operates by optimizing the margin between different classes in a dataset. The key idea behind SVM is to find the hyperplane that maximizes the margin – the distance between the closest points of the classes, known as support vectors. By maximizing this margin, SVM aims to improve the generalization of the model on unseen data, thus providing a robust classification solution.

This approach enables SVM to perform effectively even in cases where the data is not linearly separable, particularly when using kernel functions that allow for the transformation of the input space into higher dimensions. This is critical since it enables SVM to create complex decision boundaries while maintaining the principle of maximizing the margin.

In contrast, while SVM can be adapted for regression tasks, its primary strength and standard application lie in classification. The notion that SVM cannot handle non-linear data is misleading, as kernels specifically address this limitation. Additionally, SVM is not a clustering algorithm; rather, it is distinguished for its classification capabilities through margin optimization.