code: def classification_report(model, X, y=None, ax=None, classes=None,
                            random_state=None,**kwargs):
      
      # Instantiate the visualizer
      visualizer = ClassificationReport(model, ax, classes, **kwargs)
  
      # Create the train and test splits
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.2, 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
docs: Quick method:
  
      Displays precision, recall, F1, and support scores for the model.
      Integrates numerical scores as well as color-coded heatmap.
  
      This helper function is a quick wrapper to utilize the ClassificationReport
      ScoreVisualizer for one-off analysis.
  
      Parameters
      ----------
      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.
  
      model : the Scikit-Learn estimator (should be a classifier)
  
      classes : list of strings
          The names of the classes in the target
  
      random_state: integer
          The seed value for a random generator
  
      Returns
      -------
      ax : matplotlib axes
          Returns the axes that the classification report was drawn on.