What is the goal of a support vector machine (SVM) in classification tasks?

The goal of a support vector machine (SVM) in classification tasks is to maximize the margin between different classes in feature space. An SVM works by finding the hyperplane that best separates two or more classes of data points. This hyperplane is positioned in such a way that it is as far away as possible from the closest data points of each class, known as support vectors. By maximizing this margin, the SVM enhances its ability to classify unseen data with greater accuracy.

This focus on maximizing the margin contributes to a better generalization of the model, as it reduces the risk of overfitting to the training data. The farther away the classes are from each other, the more robust the model will be in distinguishing between them when new data is presented.

The other options, while related to various aspects of machine learning, do not capture the primary objective of SVMs in classification tasks. Clustering similar data points pertains to unsupervised learning techniques rather than the supervised learning nature of SVMs. Optimizing a model's hyperparameters and minimizing the variance of a training dataset are also important in the broader context of machine learning, but they do not specifically define the primary function or goal of support vector machines.