What is a support vector machine?

A support vector machine (SVM) is a type of supervised learning algorithm that can be used for classification and regression tasks. SVMs work by finding the hyperplane in a high-dimensional space that best separates the classes or predicts the target variable. The hyperplane is chosen to maximize the margin or distance between the closest data points from each class, and the algorithm is typically formulated as a convex optimization problem. SVMs are known for their ability to handle high-dimensional data and their effectiveness in complex classification tasks.