Support Vector Machine

A Support Vector Machine (SVM) is a machine learning algorithm that finds the best boundary line or surface to separate different groups of data points, similar to how you might draw a line to distinguish between two types of objects. It works by identifying the data points closest to this boundary (called support vectors) and positioning the line to maximize the distance from it to any data point, which helps the algorithm make accurate predictions on new, unseen data. SVMs are particularly useful in science and technology for tasks like classifying medical images, predicting protein structures, or sorting emails into categories because they excel at handling complex, high-dimensional data.