What is a Support Vector Machine (SVM)?

A Support Vector Machine (SVM) finds the hyperplane that maximally separates classes in feature space. The margin is the distance between the hyperplane and the nearest data points (support vectors) of each class. SVM maximizes this margin, leading to better generalization. For non-linearly separable data, the kernel trick (RBF, polynomial kernels) maps data to higher-dimensional space where it becomes separable. SVMs work well in high-dimensional spaces and with small datasets but scale poorly to very large datasets.