I would like to know if there are ways to visualize the separating hyperplane in an SVM with more than 3 features/dimensions. Normally, classification plots are possible with 1,2 and 3 dimensions (see for e.g., Noble, Nature Biotechnology 2006. Fig 1 ). Certainly, I understand that with 4 or more dimensions visualization is hard if not impossible. However, for presentation purposes it would be nice if a separating hyperplane could be visualized in some way. Other visualizations that show the quality of the result other than plotting a ROC curve are also welcome!
As example I took the Iris data from r, below reduced to two dimensions. The resulting fit can be plotted and are shown in the figure (code partly copied from ). However, how to do this if the four features, Sepal.Length, Sepal.Width, Petal.Length and Petal.Width were kept?
library(e1071) iris.part = subset(iris, Species != 'setosa') iris.part$Species = factor(iris.part$Species) iris.part = iris.part[, c(1,2,5)] fit = svm(Species ~ ., data=iris.part, type='C-classification', kernel='linear') plot(fit, iris.part)