What is the geometric interpretation of SVM?

SVM finds a hyperplane that maximizes the distance between two classes in feature space.

Geometrically, SVM constructs a separating hyperplane and two parallel margin boundaries. The optimal hyperplane lies equidistant from the closest points (support vectors) of both classes.