code: def param_projection(self, x_param, y_param, metric):
          
          return param_projection(self.estimator.cv_results_, x_param, y_param, metric)
docs: Projects the grid search results onto 2 dimensions.
  
          The wrapped GridSearch object is assumed to be fit already.
          The display value is taken as the max over the non-displayed dimensions.
  
          Parameters
          ----------
          x_param : string
              The name of the parameter to be visualized on the horizontal axis.
  
          y_param : string
              The name of the parameter to be visualized on the vertical axis.
  
          metric : string (default 'mean_test_score')
              The field from the grid search's `cv_results` that we want to display.
  
          Returns
          -------
          unique_x_vals : list
              The parameter values that will be used to label the x axis.
  
          unique_y_vals: list
              The parameter values that will be used to label the y axis.
  
          best_scores: 2D numpy array (n_y by n_x)
              Array of scores to be displayed for each parameter value pair.