classification algorithms supportmachine vector machine (svmintroduction to svm support vector machines (svmsare powerful yet flexible supervised machine learning algorithms which are used both for classification and regression but generallythey are used in classification problems in ssvms were first introduced but later they got refined in svms have their unique way of implementation as compared to other machine learning algorithms latelythey are extremely popular because of their ability to handle multiple continuous and categorical variables working of svm an svm model is basically representation of different classes in hyperplane in multidimensional space the hyperplane will be generated in an iterative manner by svm so that the error can be minimized the goal of svm is to divide the datasets into classes to find maximum marginal hyperplane (mmhmargin class -axis class support vectors -axis the followings are important concepts in svmsupport vectorsdatapoints that are closest to the hyperplane is called support vectors separating line will be defined with the help of these data points hyperplaneas we can see in the above diagramit is decision plane or space which is divided between set of objects having different classes marginit may be defined as the gap between two lines on the closet data points of different classes it can be calculated as the perpendicular distance from the line to the support vectors large margin is considered as good margin and small margin is considered as bad margin