Tuning an RBF kernel SVM
In this exercise you will build a tuned RBF kernel SVM for the given training dataset (available in dataframe trainset
) and calculate the accuracy on the test dataset (available in data frame testset
). You will then plot the tuned decision boundary against the test dataset.
This exercise is part of the course
Support Vector Machines in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
#tune model
tune_out <- ___(x = trainset[, -3], y = trainset[, 3],
gamma = 5*10^(-2:2),
cost = c(0.01, 0.1, 1, 10, 100),
type = "C-classification", kernel = ___)