class_docstr: This widget compares the standardized residuals $   ilde r_i$ versus the leverage $h_i$ of the corresponding observation $x_i$.
  Assuming that $Y=f(X)+\epsilon$ we can verify with this plot that the error terms $\epsilon_i$ are independent.
  The cook's distance is a measure to which extent some points are high leverage points and/or outliers.
  Cook's distance (function of leverage and residual) is a measure of how influential a data point is.
  If a point lies beyond of contour lines corresponding to a Cook's distance larger than 1, then this point should be
  considered as dangerously influential.
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docstr: Update the Cooks Distance widget values
  
  Parameters
  ----------
  model_leverage: np.ndarray
      An array of length $n$, containing the leverage of the observations
  cooks_distances: np.ndarray
      An array of length $n$, containing the cooks_distances of the observations
  standardized_residuals: np.ndarray
      An array of length $n$, containing the standardized residuals
  n_model_params: int
      The number of parameters of the used model
  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. This attribute must have the same dimensionality
      as the model_leverage and standardized_residuals array. Each entry in this array must be one of the
      strings ['train', 'test'] to denote from which split this observation originates.
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code: def update_values(
      self,
      model_leverage: np.ndarray,
      cooks_distances: np.ndarray,
      standardized_residuals: np.ndarray,
      n_model_params: int,
      split_origin: np.ndarray = None,
  ):
      """
      Update the Cooks Distance widget values
  
      Parameters
      ----------
      model_leverage: np.ndarray
          An array of length $n$, containing the leverage of the observations
      cooks_distances: np.ndarray
          An array of length $n$, containing the cooks_distances of the observations
      standardized_residuals: np.ndarray
          An array of length $n$, containing the standardized residuals
      n_model_params: int
          The number of parameters of the used model
      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. This attribute must have the same dimensionality
          as the model_leverage and standardized_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.__cooks_distance_plot(
          model_leverage,
          cooks_distances,
          standardized_residuals,
          n_model_params,
          split_origin=split_origin,
      )
      self.update({"data": self._plot.data}, overwrite=True)
      self.update_layout()
  
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 3 for calling the CooksDistanceWidget obj's underlying member method code with example usage: obj.update_values(model_leverage, cooks_distances, standardized_residuals, n_model_params, split_origin) without return types