class_docstr: Base class for regressor model selection.
  
  The RegressionScoreVisualizer wraps a regression model to produce a
  visualization when the score method is called, usually to allow the user
  to effectively compare the performance between models.
  
  The base class provides helper functionality to ensure that regression
  visualizers consistently store the trained score for access post visualization
  and that a correct regressor is passed to the visualizer.
  
  Parameters
  ----------
  estimator : estimator
      A scikit-learn estimator that should be a regressor. If the model is
      not a regressor, an exception is raised.
  
  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).
  
  force_model : bool, default: False
      Do not check to ensure that the underlying estimator is a classifier. This
      will prevent an exception when the visualizer is initialized but may result
      in unexpected or unintended behavior.
  
  kwargs: dict
      Keyword arguments passed to the super class.
  
  Attributes
  ----------
  score_ : float
      An evaluation metric of the regressor on test data produced when
      ``score()`` is called. This metric is between 0 and 1 -- higher scores are
      generally better. For regressors, this score is usually the r2_score, but
      ensure you check the underlying model for more details about the metric.
code: def __init__(
      self, estimator, ax=None, fig=None, force_model=False, **kwargs
  ):
      if not force_model and not isregressor(estimator):
          raise YellowbrickTypeError(
              "This estimator is not a regressor; try a classifier or "
              "clustering score visualizer instead!"
          )
  
      self.force_model = force_model
      super(RegressionScoreVisualizer, self).__init__(
          estimator, ax=ax, fig=fig, **kwargs
      )
  
imports: from ..utils import isregressor
  from ..base import ScoreVisualizer
  from ..exceptions import YellowbrickTypeError
label_desc: Use image node_id 1 to create a new RegressionScoreVisualizer object from inherited base classes: ScoreVisualizer with example: obj = RegressionScoreVisualizer(estimator, ax, fig, force_model)