Support Vector Machine (SVM)

Support Vector Machines (SVMs) are powerful supervised learning models used for classification and regression analysis. The core objective of an SVM is to find a hyperplane in an N-dimensional space that distinctly classifies the data points. The algorithm seeks the hyperplane with the maximum margin, which is the maximum distance between data points of the classes. This margin is defined by the closest points to the hyperplane, known as support vectors. SVMs are particularly effective in high-dimensional spaces and can handle non-linear classification using the kernel trick, which transforms the input data into a higher-dimensional space where a linear separator can be found.