Which type of algorithms does a support vector machine belong to?

A support vector machine (SVM) is a versatile algorithm that can be used for both classification and regression tasks, which is why the correct answer encompasses both categories.

In the context of classification, SVM is utilized to find the optimal hyperplane that separates different classes in the feature space. The goal is to maximize the margin between the closest points (support vectors) of each class. This makes SVM particularly powerful for tasks where categories need clear boundaries.

On the other hand, SVM can also be adapted for regression, known as Support Vector Regression (SVR). In this scenario, the algorithm works to find a function that deviates from actual target values at most by a specified margin, providing a robust approach to regression problems even in cases with noise.

This dual capability to tackle both classification and regression problems makes SVM a widely applicable and effective tool in the field of data science, hence the answer that identifies both classification and regression algorithms as correct acknowledges its full range of application.