class_docstr: Create a color plot showing the best grid search scores across two
  parameters.
  
  Parameters
  ----------
  estimator : Scikit-Learn grid search object
      Should be an instance of GridSearchCV. If not, an exception is raised.
  
  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.
  
  ax : matplotlib Axes, default: None
      The axes to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  colormap : string or cmap, default: 'RdBu_r'
      optional string or matplotlib cmap to colorize lines
      Use either color to colorize the lines on a per class basis or
      colormap to color them on a continuous scale.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Examples
  --------
  >>> from yellowbrick.gridsearch import GridSearchColorPlot
  >>> from sklearn.model_selection import GridSearchCV
  >>> from sklearn.svm import SVC
  >>> gridsearch = GridSearchCV(SVC(),
                                {'kernel': ['rbf', 'linear'], 'C': [1, 10]})
  >>> model = GridSearchColorPlot(gridsearch, x_param='kernel', y_param='C')
  >>> model.fit(X)
  >>> model.show()
code: def finalize(self):
      self.set_title("Grid Search Scores")
      self.ax.set_xlabel(self.x_param)
      self.ax.set_ylabel(self.y_param)
  
file_path: yellowbrick/yellowbrick/gridsearch/pcolor.py
imports: import numpy
  from .base import GridSearchVisualizer
label_desc: Use image node_id 3 for calling the GridSearchColorPlot obj's underlying member method code with example usage: obj.finalize() without return types