class_docstr: Visualizations of instances in feature space.
  
  Data Visualizers plot instances in feature space (sometimes also referred
  to as data space). Feature space is a multi-dimensional space defined by
  the columns of the dataset ``X`` when passed to ``fit()`` and ``transform``.
  These instances and their features are directly plotted in a representation
  of the higher dimensional space.
  
  Instances can also be labeled by an target vectory, ``y``. The target is
  visualized in data space by color. For example a discrete target for
  classification problems will use categorical colors and a legend. A
  continuous target for regression problems will use sequential colors with
  a colormap.
  
  This class provides helper functionality related to target identification:
  whether or not the target is sequential or categorical, and mapping a
  color sequence or color set to the targets as appropriate. It also
  determines the scope of the target, e.g. the unique classes or the range
  of the dataset for use in specific visualizations.
  
  Parameters
  ----------
  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).
  
  features : list, default: None
      The names of the features specified by the columns of the input dataset.
      This length of this list must match the number of columns in X, otherwise
      an exception will be raised on ``fit()``.
  
  classes : list, default: None
      The class labels for each class in y, ordered by sorted class index. These
      names act as a label encoder for the legend, identifying integer classes
      or renaming string labels. If omitted, the class labels will be taken from
      the unique values in y.
  
      Note that the length of this list must match the number of unique values in
      y, otherwise an exception is raised. This parameter is only used in the
      discrete target type case and is ignored otherwise.
  
  colors : list or tuple, default: None
      A single color to plot all instances as or a list of colors to color each
      instance according to its class in the discrete case or as an ordered
      colormap in the sequential case. If not enough colors per class are
      specified then the colors are treated as a cycle.
  
  colormap : string or cmap, default: None
      The colormap used to create the individual colors. In the discrete case
      it is used to compute the number of colors needed for each class and
      in the continuous case it is used to create a sequential color map based
      on the range of the target.
  
  target_type : str, default: "auto"
      Specify the type of target as either "discrete" (classes) or "continuous"
      (real numbers, usually for regression). If "auto", then it will
      attempt to determine the type by counting the number of unique values.
  
      If the target is discrete, the colors are returned as a dict with classes
      being the keys. If continuous the colors will be list having value of
      color for each point. In either case, if no target is specified, then
      color will be specified as the first color in the color cycle.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  ----------
  features_ : ndarray, shape (n_features,)
      The names of the features discovered or used in the visualizer that
      can be used as an index to access or modify data in X. If a user passes
      feature names in, those features are used. Otherwise the columns of a
      DataFrame are used or just simply the indices of the data array.
  
  classes_ : ndarray, shape (n_classes,)
      The class labels that define the discrete values in the target. Only
      available if the target type is discrete. This is guaranteed to be
      strings even if the classes are a different type.
  
  range_ : (min y, max y)
      A tuple that describes the minimum and maximum values in the target.
      Only available if the target type is continuous.
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code: def __init__(
      self,
      ax=None,
      fig=None,
      features=None,
      classes=None,
      colors=None,
      colormap=None,
      target_type="auto",
      **kwargs,
  ):
      super(DataVisualizer, self).__init__(
          ax=ax, fig=fig, features=features, **kwargs
      )
  
      # Validate raises YellowbrickValueError if invalid
      TargetType.validate(target_type)
  
      # Data Parameters
      self.classes = classes
      self.target_type = target_type
  
      # Visual Parameters
      self.colors = colors
      self.colormap = colormap
  
      # Internal attributes
      self._colors = None
      self._target_color_type = None
      self._label_encoder = None
  
imports: import numpy
  import matplotlib
  from yellowbrick.base import Visualizer
  from yellowbrick.utils import is_dataframe
  from yellowbrick.style import resolve_colors
  from yellowbrick.exceptions import NotFitted
  from yellowbrick.utils.target import target_color_type, TargetType
  from yellowbrick.exceptions import YellowbrickKeyError, YellowbrickValueError
  from yellowbrick.style import palettes
  from matplotlib.colors import Normalize
  from sklearn.base import TransformerMixin
label_desc: Use image node_id 1 to create a new DataVisualizer object from inherited base classes: MultiFeatureVisualizer with example: obj = DataVisualizer(ax, fig, features, classes, colors, colormap, target_type)