Part 2: Both options are acceptable. ]]>

in part 1, we should use cross validation for every d and c, and plot for every d the average error (over the 10-folds) as a function of c. Correct?

In part 2, we have to count the number of support vectors that lie on the margin hyperplane. Should we write the average of the number of support vectors on the margin hyperplane (over the 10 classifiers we create), or train a new classifier, that trains on all the data, and write the number of support vectors that are on the margin hyperplane for that classifier?

The same goes for computing the margin - is this the average margin size of the 10 margins we created?