Tuning a support vector machine
Besides using different feature sets and the kernel function in support vector machines, one trick that you can use to tune its performance is to adjust the gamma and cost configured in the argument. One possible approach to test the performance of different gamma and cost combination values is to write a for loop to generate all the combinations of gamma and cost as inputs to train different support vector machines. Fortunately, SVM provides a tuning function, tune.svm, which makes the tuning much easier. In this recipe, we will demonstrate how to tune a support vector machine through the use of tune.svm.
Getting ready
Before completing this recipe, you need to have completed the previous recipe by preparing a training dataset, trainset.
How to do it...
Perform the following steps to tune the support vector machine:
- First, tune the support vector machine using
tune.svm:
> tuned = tune.svm(churn~., data = trainset, gamma = 10^(-6:-1),
cost = 10...
