class_docstr: Parameters
  ----------
  ax : matplotlib Axes, default: None
      The aces to plot the figure on
  
  scale : bool, default: True
      Boolean that indicates if the values of X should be scaled.
  
  colormap : string or cmap, default: None
      optional string or matplotlib cmap to colorize lines
      Use either color to colorize the lines on a per class basis or
      colormap to color them on a continuous scale.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  
  Examples
  --------
  
  >>> visualizer = ExplainedVariance()
  >>> visualizer.fit(X)
  >>> visualizer.transform(X)
  >>> visualizer.show()
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code: def __init__(
      self,
      ax=None,
      scale=True,
      center=True,
      n_components=None,
      colormap=palettes.DEFAULT_SEQUENCE,
      **kwargs,
  ):
      super(ExplainedVariance, self).__init__(ax=ax, **kwargs)
  
      self.colormap = colormap
      self.n_components = n_components
      self.center = center
      self.scale = scale
      self.pipeline = Pipeline(
          [
              (
                  "scale",
                  StandardScaler(
                      with_mean=self.center, with_std=self.scale
                  ),
              ),
              ("pca", PCA(n_components=self.n_components)),
          ]
      )
      self.pca_features = None
  
file_path: yellowbrick/yellowbrick/features/decomposition.py
imports: from yellowbrick.style import palettes
  from yellowbrick.features.base import FeatureVisualizer
  from sklearn.pipeline import Pipeline
  from sklearn.decomposition import PCA
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 1 to create a new ExplainedVariance object from inherited base classes: FeatureVisualizer with example: obj = ExplainedVariance(ax, scale, center, n_components, colormap)