Support Vector Machines (SVMs) are supervised learning models used for classification and regression analysis. They are particularly effective in high-dimensional spaces and are known for their ability to find an optimal hyperplane that separates different classes of data with maximum margin. In the context of catalysis, SVMs can be employed to analyze and predict the performance of various catalytic systems.
