code: def svc_cv(C, gamma, data, targets):
      
      estimator = SVC(C=C, gamma=gamma, random_state=2)
      cval = cross_val_score(estimator, data, targets, scoring='roc_auc', cv=4)
      return cval.mean()
docs: SVC cross validation.
  
      This function will instantiate a SVC classifier with parameters C and
      gamma. Combined with data and targets this will in turn be used to perform
      cross validation. The result of cross validation is returned.
  
      Our goal is to find combinations of C and gamma that maximizes the roc_auc
      metric.