class_docstr: Defines the ErrorReport, which contains the tree and matrix filter.
  
  :param tree: The representation of the tree.
  :type tree: dict
  :param matrix: The representation of the matrix filter.
  :type matrix: dict
  :param id: The unique identifier for the ErrorReport.
      A new unique id is created if none is specified.
  :type id: str
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docstr: Defines the ErrorReport, which contains the tree and matrix filter.
  
  :param tree: The representation of the tree.
  :type tree: dict
  :param matrix: The representation of the matrix filter.
  :type matrix: dict
  :param tree_features: The features that were selected to train the
      decision tree on errors.
  :type tree_features: list[str]
  :param matrix_features: The one or two features selected for creating
      the matrix filter (aka heatmap).
  :type matrix_features: list[str]
  :param importances: The feature importances calculated using mutual
      information with the error on the true labels.
  :type importances: list[float]
  :param root_stats: The statistics for the root all data cohort.
  :type root_stats: dict
  :param id: The unique identifier for the ErrorReport.
      A new unique id is created if none is specified.
  :type id: str
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code: def __init__(
      self,
      tree,
      matrix,
      tree_features=None,
      matrix_features=None,
      importances=None,
      root_stats=None,
      id=None,
  ):
      """Defines the ErrorReport, which contains the tree and matrix filter.
  
      :param tree: The representation of the tree.
      :type tree: dict
      :param matrix: The representation of the matrix filter.
      :type matrix: dict
      :param tree_features: The features that were selected to train the
          decision tree on errors.
      :type tree_features: list[str]
      :param matrix_features: The one or two features selected for creating
          the matrix filter (aka heatmap).
      :type matrix_features: list[str]
      :param importances: The feature importances calculated using mutual
          information with the error on the true labels.
      :type importances: list[float]
      :param root_stats: The statistics for the root all data cohort.
      :type root_stats: dict
      :param id: The unique identifier for the ErrorReport.
          A new unique id is created if none is specified.
      :type id: str
      """
      self._id = id or str(uuid.uuid4())
      self._tree = tree
      self._matrix = matrix
      self._tree_features = tree_features
      self._matrix_features = matrix_features
      self._importances = importances
      self._root_stats = root_stats
      self._metadata = {_VERSION: _ErrorReportVersion4}
  
file_path: responsible-ai-toolbox/erroranalysis/erroranalysis/report/error_report.py
imports: import json
  import uuid
  from raiutils.data_processing import serialize_json_safe
label_desc: Use image node_id 1 to create a new ErrorReport object from inherited base classes: object with example: obj = ErrorReport(tree, matrix, tree_features, matrix_features, importances, root_stats, id)