code: def svc_cv(C, gamma, data, targets):
      """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.
      """
      estimator = SVC(C=C, gamma=gamma, random_state=2)
      cval = cross_val_score(
          estimator, data, targets, scoring="roc_auc", cv=4
      )
      return cval.mean()
  
file_path: BayesianOptimization/examples/sklearn_example.py
imports: from sklearn.datasets import make_classification
  from sklearn.model_selection import cross_val_score
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.svm import SVC
  from bayes_opt import BayesianOptimization
  from bayes_opt.util import Colours
label_desc: Use image node_id 2 for calling a global function with example usage: svc_cv(C, gamma, data, targets) and returns: cval