text: Title: Stacked bar plots helper function
  Body: Create a helper function in the `yellowbrick.draw` module that can create a stacked bar chart from a 2D array on the specified axes. This function should have the following basic signature:
  
  ```python
  def bar_stack(data, ax=None, labels=None, ticks=None, colors=None, **kwargs):
      """
      An advanced bar chart plotting utility that can draw bar and stacked bar charts from
      data, wrapping calls to the specified matplotlib.Axes object. 
  
      Parameters
      ----------
      data : 2D array-like 
          The data associated with the bar chart where the columns represent each bar
          and the rows represent each stack in the bar chart. A single bar chart would 
          be a 2D array with only one row, a bar chart with three stacks per bar would 
          have a shape of (3, b).  
  
      ax : matplotlib.Axes, default: None
          The axes object to draw the barplot on, uses plt.gca() if not specified.
  
      labels : list of str, default: None
          The labels for each row in the bar stack, used to create a legend. 
  
      ticks : list of str, default: None
          The labels for each bar, added to the x-axis for a vertical plot, or the y-axis
          for a horizontal plot.  
  
      colors : array-like, default: None
          Specify the colors of each bar, each row in the stack, or every segment. 
  
      kwargs : dict 
          Additional keyword arguments to pass to ``ax.bar``. 
      """
  ```
  
  This is just a base signature and should be further refined as we dig into the work. Once this helper function is created, the following visualizers should be updated to use it:
  
  - [ ] `PosTagVisualizer`
  - [ ] `FeatureImportances`
  - [ ] `ClassPredictionError`
  
  (@DistrictDataLabs/team-oz-maintainers please feel free to add other visualizers above as needed)
  
  **Describe the solution you'd like**
  A helper function to create stacked bar plot. (See #510 and #847, #771 )
  
  **Is your feature request related to a problem? Please describe.**
  This can be useful for `ClassPredictionError`, `FeatureImportances`, and future stacked bar plot.