$8.$ This problem involves the OJ data set which is part of the ISLR$2$ package.

$($a$)$ Create a training set containing a random sample of $800$ observations, and a test set containing the remaining observations.

$($b$)$ Fit a support vector classifier to the training data using cost $=0\mathrm{.}01,$ with Purchase as the response and the other variables as predictors. Use the summary$()$ function to produce summary statistics, and describe the results obtained.

$($c$)$ What are the training and test error rates?

$($d$)$ Use the tune$()$ function to select an optimal cost. Consider values in the range $0\mathrm{.}01$ to $10.$

$($e$)$ Compute the training and test error rates using this new value for cost.

$($f$)$ Repeat parts $($b$)$ through $($e$)$ using a support vector machine with a radial kernel. Use the default value for gamma.

$($g$)$ Repeat parts $($b$)$ through $($e$)$ using a support vector machine with a polynomial kernel. Set degree $=2.$

$($h$)$ Overall, which approach seems to give the best results on this data?