class_docstr: The Tunkey Anscombe Plot compares the residuals $r_i=y_i-\hat y_i$ versus the predicted values $\hat y_i$.
  Assuming that $Y=f(X)+\epsilon$ we can verify with this plot that $E[\epsilon]=0$.
  The fitted trend line should follow a straight line along the axis of predicted values at the position 0 on the
  residual axis.
class_docstr_tok: ["The","Tunkey","Anscombe","Plot","compares","the","residuals","$","r_i=y_i-\\hat","y_i","$","versus","the","predicted","values","$","\\hat","y_i","$",".","Assuming","that","$","Y=f","(","X",")","+\\epsilon","$","we","can","verify","with","this","plot","that","$","E","[","\\epsilon","]","=0","$",".","The","fitted","trend","line","should","follow","a","straight","line","along","the","axis","of","predicted","values","at","the","position","0","on","the","residual","axis","."]
docstr: Instantiates the Tunkey Anscombe plot
  
  Parameters
  ----------
  predictions: np.ndarray
      The prediction of a model on some data
  residuals: np.ndarray
      The residuals / error of the predictions when compared to the true value
  split_origin: np.ndarray
      Optional, if the data used for the predictions includes unseen test data.
      These residuals can be marked explicitly in the plot. To do this attribute must have the same dimensionality
      as the predictions and residuals array. Each entry in this array must be one of the strings ['train', 'test']
      to denote from which split this observation originates.
docstr_tok: ["Instantiates","the","Tunkey","Anscombe","plot","Parameters","--","--","--","--","--","predictions",":","np.ndarray","The","prediction","of","a","model","on","some","data","residuals",":","np.ndarray","The","residuals","\/","error","of","the","predictions","when","compared","to","the","true","value","split_origin",":","np.ndarray","Optional",",","if","the","data","used","for","the","predictions","includes","unseen","test","data",".","These","residuals","can","be","marked","explicitly","in","the","plot",".","To","do","this","attribute","must","have","the","same","dimensionality","as","the","predictions","and","residuals","array",".","Each","entry","in","this","array","must","be","one","of","the","strings","[","'train","'",",","'test","'","]","to","denote","from","which","split","this","observation","originates","."]
code: def __init__(
      self,
      predictions: np.ndarray,
      residuals: np.ndarray,
      split_origin: np.ndarray = None,
      **kwargs,
  ):
      """
      Instantiates the Tunkey Anscombe plot
  
      Parameters
      ----------
      predictions: np.ndarray
          The prediction of a model on some data
      residuals: np.ndarray
          The residuals / error of the predictions when compared to the true value
      split_origin: np.ndarray
          Optional, if the data used for the predictions includes unseen test data.
          These residuals can be marked explicitly in the plot. To do this attribute must have the same dimensionality
          as the predictions and residuals array. Each entry in this array must be one of the strings ['train', 'test']
          to denote from which split this observation originates.
      """
  
      self._plot = self.__tukey_anscombe_plot(
          predictions, residuals, split_origin
      )
      super(TukeyAnscombeWidget, self).__init__(self._plot, **kwargs)
  
imports: from typing import List, Optional
  import numpy
  import pandas
  import plotly.express
  import plotly.graph_objects
  from ipywidgets import Layout, widgets
  from plotly.basewidget import BaseFigureWidget
  from scipy import stats
  import pycaret.internal.plots.helper
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.display.display_backend import ColabBackend, DatabricksBackend
  from pycaret.internal.logging import get_logger
  from pycaret.internal.validation import fit_if_not_fitted
label_desc: Use image node_id 1 to create a new TukeyAnscombeWidget object from inherited base classes: BaseFigureWidget with example: obj = TukeyAnscombeWidget(predictions, residuals, split_origin)