docs: def class_prediction_error(
      model,
      X,
      y=None,
      ax=None,
      classes=None,
      test_size=0.2,
      random_state=None,
      **kwargs):
      """Quick method:
      Divides the dataset X and y into train and test splits, fits the model on
      the train split, then scores the model on the test split. The visualizer
      displays the support for each class in the fitted classification model
      displayed as a stacked bar plot Each bar is segmented to show the
      distribution of predicted classes for each class.
  
      This helper function is a quick wrapper to utilize the ClassPredictionError
      ScoreVisualizer for one-off analysis.
  
      Parameters
      ----------
      model : the Scikit-Learn estimator (should be a classifier)
  
      X  : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features.
  
      y  : ndarray or Series of length n
          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
  
      test_size : float, default=0.2
          The percentage of the data to reserve as test data.
  
      random_state : int or None, default=None
          The value to seed the random number generator for shuffling data.
  
      Returns
      -------
      ax : matplotlib axes
          Returns the axes that the class prediction error plot was drawn on.
      """
      # Instantiate the visualizer
      visualizer = ClassPredictionError(model, ax, classes, **kwargs)
  
      # Create the train and test splits
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=test_size, random_state=random_state
      )
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X_train, y_train, **kwargs)
      visualizer.score(X_test, y_test)
  
      # Return the axes object on the visualizer
      return visualizer.ax