12 Support Vector Machines in R SVM classification plot 2 44 1.0 △△ △▲ 1 △△◆ 0.5 0.0 0 ● o -0.5 88 -1.0 -2 -1.5 0 X2 Figure 1:A contour plot of the fitted decision values for a simple binary classification problem. complemented by visualization and tuning functions.libsvm is a fast and easy-to-use imple- mentation of the most popular SVM formulations(C and v classification,e and v regression, and novelty detection).It includes the most common kernels (linear,polynomial,RBF,and sigmoid),only extensible by changing the C++source code of libsvm.Multi-class classifica- tion is provided using the one-against-one voting scheme.Other features include the computa- tion of decision and probability values for predictions(for both classification and regression), shrinking heuristics during the fitting process,class weighting in the classification mode,han- dling of sparse data,and the computation of the training error using cross-validation.libsvm is distributed under a very permissive,BSD-like licence. The R implementation is based on the S3 class mechanisms.It basically provides a training function with standard and formula interfaces,and a predict()method.In addition,a plot()method visualizing data,support vectors,and decision boundaries if provided.Hyper- parameter tuning is done using the tune()framework in el071 performing a grid search over specified parameter ranges. The sample session starts with a C classification task on the iris data,using the radial basis function kernel with fixed hyper-parameters C and y: library("e1071") model <-svm(Species ~.data iris_train, + method "C-classification",kernel "radial", cost =10,gamma =0.1) summary(model) Call:12 Support Vector Machines in R −1.5 −1.0 −0.5 0.0 0.5 1.0 −1 0 1 2 −2 −1 0 1 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SVM classification plot X2 X1 Figure 1: A contour plot of the fitted decision values for a simple binary classification problem. complemented by visualization and tuning functions. libsvm is a fast and easy-to-use imple￾mentation of the most popular SVM formulations (C and ν classification,  and ν regression, and novelty detection). It includes the most common kernels (linear, polynomial, RBF, and sigmoid), only extensible by changing the C++ source code of libsvm. Multi-class classifica￾tion is provided using the one-against-one voting scheme. Other features include the computa￾tion of decision and probability values for predictions (for both classification and regression), shrinking heuristics during the fitting process, class weighting in the classification mode, han￾dling of sparse data, and the computation of the training error using cross-validation. libsvm is distributed under a very permissive, BSD-like licence. The R implementation is based on the S3 class mechanisms. It basically provides a training function with standard and formula interfaces, and a predict() method. In addition, a plot() method visualizing data, support vectors, and decision boundaries if provided. Hyper￾parameter tuning is done using the tune() framework in e1071 performing a grid search over specified parameter ranges. The sample session starts with a C classification task on the iris data, using the radial basis function kernel with fixed hyper-parameters C and γ: > library("e1071") > model <- svm(Species ~ ., data = iris_train, + method = "C-classification", kernel = "radial", + cost = 10, gamma = 0.1) > summary(model) Call: