Using `tune.svm()`
This exercise will give you hands-on practice with using the tune.svm()
function. You will use it to obtain the optimal values for the cost
, gamma
, and coef0
parameters for an SVM model based on the radially separable dataset you created earlier in this chapter. The training data is available in the dataframe trainset
, the test data in testset
, and the e1071
library has been preloaded for you. Remember that the class variable y
is stored in the third column of the trainset
and testset
.
Also recall that in the video, Kailash used cost=10^(1:3)
to get a range of the cost parameter from 10=10^1
to 1000=10^3
in multiples of 10.
This is a part of the course
“Support Vector Machines in R”
Exercise instructions
- Set parameter search ranges as follows:
cost
- from 0.1 (10^(-1)
) to 100 (10^2
) in multiples of 10.gamma
andcoef0
- one of the following values: 0.1, 1 and 10.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
#tune model
tune_out <-
tune.svm(x = trainset[, -3], y = trainset[, 3],
type = "C-classification",
kernel = "polynomial", degree = 2, cost = 10^(___:___),
gamma = c(___, ___, ___), coef0 = c(0.1, 1, 10))
#list optimal values
tune_out$best.parameters$___
tune_out$best.parameters$___
tune_out$best.parameters$___