code: def gridsearch_color_plot(
      estimator, x_param, y_param, X=None, y=None, ax=None, **kwargs
  ):
      """Quick method:
      Create a color plot showing the best grid search scores across two
      parameters.
  
      This helper function is a quick wrapper to utilize GridSearchColorPlot
      for one-off analysis.
  
      If no `X` data is passed, the model is assumed to be fit already. This
      allows quick exploration without waiting for the grid search to re-run.
  
      Parameters
      ----------
      estimator : Scikit-Learn grid search object
          Should be an instance of GridSearchCV. If not, an exception is raised.
          The model may be fit or unfit.
  
      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.
  
      X  : ndarray or DataFrame of shape n x m or None (default None)
          A matrix of n instances with m features. If not None, forces the
          GridSearchCV object to be fit.
  
      y  : ndarray or Series of length n or None (default None)
          An array or series of target or class values.
  
      ax : matplotlib axes
          The axes to plot the figure on.
  
      classes : list of strings
          The names of the classes in the target
  
      Returns
      -------
      ax : matplotlib axes
          Returns the axes that the classification report was drawn on.
      """
      # Instantiate the visualizer
      visualizer = GridSearchColorPlot(
          estimator, x_param, y_param, ax=ax, **kwargs
      )
  
      # Fit if necessary
      if X is not None:
          visualizer.fit(X, y)
      else:
          visualizer.draw()
  
      # Return the axes object on the visualizer
      return visualizer.ax
  
file_path: yellowbrick/yellowbrick/gridsearch/pcolor.py
imports: import numpy
  from .base import GridSearchVisualizer
label_desc: Use image node_id 1 for calling a global function with example usage: gridsearch_color_plot(estimator, x_param, y_param, X, y, ax) and returns: visualizer