class_docstr: BalancedBinningReference generates a histogram with vertical lines
  showing the recommended value point to bin your data so they can be evenly
  distributed in each bin.
  
  Parameters
  ----------
  ax : matplotlib Axes, default: None
      This is inherited from FeatureVisualizer and is defined within
      ``BalancedBinningReference``.
  
  target : string, default: "y"
      The name of the ``y`` variable
  
  bins : number of bins to generate the histogram, default: 4
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  bin_edges_ : binning reference values
  
  Examples
  --------
  >>> visualizer = BalancedBinningReference()
  >>> visualizer.fit(y)
  >>> visualizer.show()
  
  
  Notes
  -----
  These parameters can be influenced later on in the visualization
  process, but can and should be set as early as possible.
docstr: Sets up y for the histogram and checks to
  ensure that ``y`` is of the correct data type.
  Fit calls draw.
  
  Parameters
  ----------
  y : an array of one dimension or a pandas Series
  
  kwargs : dict
      keyword arguments passed to scikit-learn API.
code: def fit(self, y, **kwargs):
      """
      Sets up y for the histogram and checks to
      ensure that ``y`` is of the correct data type.
      Fit calls draw.
  
      Parameters
      ----------
      y : an array of one dimension or a pandas Series
  
      kwargs : dict
          keyword arguments passed to scikit-learn API.
  
      """
  
      # throw an error if y has more than 1 column
      if y.ndim > 1:
          raise YellowbrickValueError(
              "y needs to be an array or Series with one dimension"
          )
  
      # Handle the target name if it is None.
      if self.target is None:
          self.target = "y"
  
      self.draw(y)
      return self
  
file_path: yellowbrick/yellowbrick/target/binning.py
imports: import numpy
  from yellowbrick.target.base import TargetVisualizer
  from yellowbrick.exceptions import YellowbrickValueError
label_desc: Use image node_id 3 for calling the BalancedBinningReference obj's underlying member method code with example usage: obj.fit(y) and returns: self