Support Vector Machines (SVMs) are one of the most powerful and versatile supervised machine learning algorithms. Initially famous for their high-performance “out of the box,” they are capable of performing both linear and non-linear classification, regression, and outlier detection.

For classification tasks, the core idea behind SVM is to find the optimal hyperplane that best separates the different classes in the feature space.