I have a few questions about the implementation of thesupervised classification method SVM. I'm trying to run grid search crossvalidation using python scikitlearn in order to identify the optimal C andgamma parameters for the RBF kernel. Here are my specific questions: 1) Can you confirm that SVM_DO_IT is based on the libsvm? Ifso, I should be able to use scikitlearn for cross validation since it is alsobased on libsvm. 2) Can you explain the data normalization procedure in moredetail? I understand that SVM_DO_IT uses a "linear normalization".What range are the values normalized to? [0,1] or [-1,1]? It's my understandingthat we need to scale training data used for cross validation in the samemanner as SVM_DO_IT so that the C and gamma parameters determined by crossvalidation can be used. 3) Assuming SVM_DO_IT is based on libsvm, are there anyother important differences I should be aware of as they pertain to crossvalidation?