Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to tune the support vector machine:

1. First, tune the support vector machine using tune.svm:

        > tuned = tune.svm(churn~., data = trainset, gamma = 10^(-6:-1),        cost = 10^(1:2))

2. Next, you can use the summary function to obtain the tuning result:

       > summary(tuned)        Output        Parameter tuning of 'svm':        - sampling method: 10-fold cross validation         - best parameters:        gamma cost        0.01 100        - best performance: 0.08077885         - Detailed performance results:           gamma cost error dispersion        1 1e-06 10 0.14774780 0.02399512        2 1e-05 10 0.14774780 0.02399512        3 1e-04 10 0.14774780 0.02399512        4 1e-03 10 0.14774780 0.02399512        5 1e-02 10 0.09245223 0.02046032        6 1e-01 10 0.09202306 0.01938475 7 1e-06 100 0.14774780 0.02399512 ...