what is hyperplane in svm

In Support Vector Machines (SVM), a hyperplane is a decision boundary that helps classify data points. The objective of SVM is to find a hyperplane in an N-dimensional space (N being the number of features) that distinctly classifies the data points. The hyperplane separates the data between two classes, and the support vectors are the data points that are close to the decision boundary and are the most difficult to classify. The optimal hyperplane is the one that maximizes the distance to the closest data points from both classes, which is known as the hyperplane with maximum margin. The margin is the distance between the separating hyperplane and the nearest support vector. The best hyperplane is the one whose margin is the maximum, and it should be farthest from the support vectors. The dimension of the hyperplane depends on the number of features, and if the number of input features is 2, then the hyperplane is just a line. If the number of input features is 3, then the hyperplane becomes a two-dimensional plane. It becomes difficult to imagine when the number of features exceeds 3.