Support Vector Machine Learning Algorithm Concepts of Support Vector Machines The supervised learning technique Support Vector Machines (SVM) can be applied to both classification and regression applications. Finding the ideal hyperplane that categorizes the data points is how it operates. The hyperplane is chosen such that the margin between the hyperplane and the closest points of each class is maximized. Here is an example of how SVM works : Suppose we have a dataset of students categorized into two classes based on their grades in two subjects, say Physics and Math. We want to predict whether a new student will pass or fail based on their grades in Physics and Math. We use SVM to identify the optimal hyperplane that separates the passing and failing students. The hyperplane is chosen such that the margin between the hyperplane and the closest points of each class is maximized. The new student is then assigned to the class on the other side of the hyperplane. Support Vector Machin