Support Vector Machines (SVMs) are supervised machine learning models used for classification and regression tasks. SVMs work by finding the optimal hyperplane that best separates different classes in the feature space. They are particularly effective in high-dimensional spaces and are known for their robustness in handling complex datasets.
