Support vector machine

Support vector machine is another supervised learning algorithm that can be used for classification and regression. It is able to classify data linearly and nonlinearly using kernel methods. Each data point in the training dataset is labeled, as it is supervised learning, and mapped to the input feature space, and the aim is to classify every point of new data to one of the classes. A data point is an N dimension number, as N is the number of features, and the problem is to separate this data using N-1 dimensional hyperplane and this is considered to be a linear classifier. There might be many classifiers which segregate the data; however, the optimal classifier is one which has the maximum margin between classes. The maximum margin hyperplane is one which has the maximum distance from the closest point in each size and the corresponding classifier is called the maximum margin classifier. Package e1071 has all functionalities related to the support vector machine so...