docs: def prediction_error(model, X, y=None, ax=None, alpha=0.75, **kwargs):
      """
      Quick method:
  
      Plot the actual targets from the dataset against the
      predicted values generated by our model(s).
  
      This helper function is a quick wrapper to utilize the PredictionError
      ScoreVisualizer for one-off analysis.
  
      Parameters
      ----------
      model : the Scikit-Learn estimator (should be a regressor)
  
      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.
  
      shared_limits : bool, default: True
          If shared_limits is True, the range of the X and Y axis limits will
          be identical, creating a square graphic with a true 45 degree line.
          In this form, it is easier to diagnose under- or over- prediction,
          though the figure will become more sparse. To localize points, set
          shared_limits to False, but note that this will distort the figure
          and should be accounted for during analysis.
  
      besfit : bool, default: True
          Draw a linear best fit line to estimate the correlation between the
          predicted and measured value of the target variable. The color of
          the bestfit line is determined by the ``line_color`` argument.
  
      identity: bool, default: True
          Draw the 45 degree identity line, y=x in order to better show the
          relationship or pattern of the residuals. E.g. to estimate if the
          model is over- or under- estimating the given values. The color of the
          identity line is a muted version of the ``line_color`` argument.
  
      point_color : color
          Defines the color of the error points; can be any matplotlib color.
  
      line_color : color
          Defines the color of the best fit line; can be any matplotlib color.
  
      alpha : float, default: 0.75
          Specify a transparency where 1 is completely opaque and 0 is completely
          transparent. This property makes densely clustered points more visible.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers.
  
      Returns
      -------
      ax : matplotlib Axes
          Returns the axes that the prediction error plot was drawn on.
      """
      # Instantiate the visualizer
      visualizer = PredictionError(model, ax, alpha=alpha, **kwargs)
  
      # Create the train and test splits
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X_train, y_train, **kwargs)
      visualizer.score(X_test, y_test)
      visualizer.finalize()
  
      # Return the axes object on the visualizer
      return visualizer.ax