Machine Learning with R Cookbook - Second Edition

How it works...

To tune the support vector machine, you can use a trial and error method to find the best gamma and cost parameters. In other words, one has to generate a variety of combinations of gamma and cost for the purpose of training different support vector machines.

In this example, we generate different gamma values from 10^-6 to 10^-1, and cost with a value of either 10 or 100. Therefore, you can use the tuning function, svm.tune, to generate 12 sets of parameters. The function then makes 10 cross-validations and outputs the error dispersion of each combination. As a result, the combination with the least error dispersion is regarded as the best parameter set. From the summary table, we found that gamma with a value of 0.01 and ...