class_docstr: The ScoreVisualizer reports the performance of a Scikit-Learn estimator
  (usually a predictive model like a regressor, classifier, or clusterer) in
  a visual manner. They hook into the Scikit-Learn pipeline through the
  ``score(X_test, y_test)`` method, reporting not just a single numeric
  score, but also a visual report of the score in model space.
  
  Parameters
  ----------
  model : a Scikit-Learn estimator
      A Scikit-Learn estimator to wrap functionality for, usually regressor,
      classifier, or clusterer predictive model. If the estimator is not fitted,
      it is fit when the visualizer is fitted, unless otherwise specified by
      ``is_fitted``.
  
  ax : matplotlib Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  fig : matplotlib Figure, default: None
      The figure to plot the Visualizer on. If None is passed in the current
      plot will be used (or generated if required).
  
  is_fitted : bool or str, default="auto"
      Specify if the wrapped estimator is already fitted. If False, the estimator
      will be fit when the visualizer is fit, otherwise, the estimator will not be
      modified. If "auto" (default), a helper method will check if the estimator
      is fitted before fitting it again.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizersself.
  
  Notes
  -----
  Score visualizers can wrap either fitted or unfitted models.
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docstr: The primary entry point for score visualizers is the score method,
  which makes predictions based on X and scores them relative to y.
  
  Returns
  -------
  score : float or array-like
      Returns the score of the underlying model, which is model-specific,
      e.g. accuracy for classifiers, R2 for regressors, etc.
code: def score(self, X, y, **kwargs):
      """
      The primary entry point for score visualizers is the score method,
      which makes predictions based on X and scores them relative to y.
  
      Returns
      -------
      score : float or array-like
          Returns the score of the underlying model, which is model-specific,
          e.g. accuracy for classifiers, R2 for regressors, etc.
      """
      raise NotImplementedError(
          "ScoreVisualizer subclasses should implement score"
      )
  
imports: import math
  import warnings
  import matplotlib.pyplot
  from sklearn.base import BaseEstimator
  from yellowbrick.utils import get_model_name
  from yellowbrick.utils.wrapper import Wrapper
  from yellowbrick.utils.helpers import check_fitted
  from yellowbrick.exceptions import YellowbrickWarning
  from yellowbrick.exceptions import YellowbrickValueError, YellowbrickTypeError
label_desc: Use image node_id 1 for calling the ScoreVisualizer obj's underlying member method code with example usage: obj.score(X, y) without return types