class_docstr: The Scale Location plot compares the square root of the absolute standardized residuals $\sqrt{|    ilde r_i|}$
  versus the predicted values $\hat y_i$.
  Assuming that $Y=f(X)+\epsilon$ we can verify with this plot that $Var[\epsilon]=\sigma^2$.
  The fitted trend line should follow a straight line along the axis of predicted values.
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docstr: Instantiates a Scale Location plot
  
  Parameters
  ----------
  predictions: np.ndarray
      The predictions on the data
  sqrt_abs_standardized_residuals: np.ndarray
      The square root of the absolute value of the standardized residuals
  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 predictions and sqrt_abs_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 __init__(
      self,
      predictions: np.ndarray,
      sqrt_abs_standardized_residuals: np.ndarray,
      split_origin: np.ndarray = None,
      **kwargs,
  ):
      """
      Instantiates a Scale Location plot
  
      Parameters
      ----------
      predictions: np.ndarray
          The predictions on the data
      sqrt_abs_standardized_residuals: np.ndarray
          The square root of the absolute value of the standardized residuals
      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 predictions and sqrt_abs_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.__scale_location_plot(
          predictions, sqrt_abs_standardized_residuals, split_origin
      )
      super(ScaleLocationWidget, 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 ScaleLocationWidget object from inherited base classes: BaseFigureWidget with example: obj = ScaleLocationWidget(predictions, sqrt_abs_standardized_residuals, split_origin)