code: def optimize_svc(data, targets):
      
      def svc_crossval(expC, expGamma):
          """Wrapper of SVC cross validation.
  
          Notice how we transform between regular and log scale. While this
          is not technically necessary, it greatly improves the performance
          of the optimizer.
          """
          C = 10 ** expC
          gamma = 10 ** expGamma
          return svc_cv(C=C, gamma=gamma, data=data, targets=targets)
  
      optimizer = BayesianOptimization(
          f=svc_crossval,
          pbounds={"expC": (-3, 2), "expGamma": (-4, -1)},
          random_state=1234,
          verbose=2
      )
      optimizer.maximize(n_iter=10)
  
      print("Final result:", optimizer.max)
docs: Apply Bayesian Optimization to SVC parameters.