class_docstr: Mondrian Tree Regressor.
  
  Parameters
  ----------
  step
      Step of the tree.
  use_aggregation
      Whether to use aggregation weighting techniques or not.
  iteration
      Number iterations to do during training.
  seed
      Random seed for reproducibility.
  
  Notes
  -----
  The Mondrian Tree Regressor is a type of decision tree that bases splitting decisions over a
  Mondrian process.
  
  References
  ----------
  [^1]: Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh. Mondrian Forests: Efficient Online Random Forests.
      arXiv:1406.2673, pages 2-4.
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code: def __init__(
      self,
      step: float = 0.1,
      use_aggregation: bool = True,
      iteration: int = 0,
      seed: int = None,
  ):
      super().__init__(
          step=step,
          loss="least-squares",
          use_aggregation=use_aggregation,
          iteration=iteration,
          seed=seed,
      )
      # Controls the randomness in the tree
      self.seed = seed
  
      # The current sample being proceeded
      self._x: dict[base.typing.FeatureName, int | float]
      # The current label index being proceeded
      self._y: base.typing.RegTarget
  
      # Initialization of the root of the tree
      # It's the root so it doesn't have any parent (hence None)
      self._root = MondrianLeafRegressor(None, 0.0, 0)
  
file_path: river/river/tree/mondrian/mondrian_tree_regressor.py
imports: from __future__ import annotations
  import math
  from river import base, utils
  from river.tree.mondrian.mondrian_tree import MondrianTree
  from river.tree.mondrian.mondrian_tree_nodes import MondrianBranchRegressor, MondrianLeafRegressor, MondrianNodeRegressor
label_desc: Use image node_id 1 to create a new MondrianTreeRegressor object from inherited base classes: MondrianTree, base with example: obj = MondrianTreeRegressor(step, use_aggregation, iteration, seed)