class_docstr: Learning Node of the Hoeffding Adaptive Tree regressor.
  
  Parameters
  ----------
  stats
      Initial class observations.
  depth
      The depth of the learning node in the tree.
  splitter
      The numeric attribute observer algorithm used to monitor target statistics
      and perform split attempts.
  drift_detector
      The detector used to monitor concept drifts.
  rng
      Random number generator used in Poisson sampling.
  kwargs
      Other parameters passed to the learning node.
class_docstr_tok: ["Learning","Node","of","the","Hoeffding","Adaptive","Tree","regressor",".","Parameters","--","--","--","--","--","stats","Initial","class","observations",".","depth","The","depth","of","the","learning","node","in","the","tree",".","splitter","The","numeric","attribute","observer","algorithm","used","to","monitor","target","statistics","and","perform","split","attempts",".","drift_detector","The","detector","used","to","monitor","concept","drifts",".","rng","Random","number","generator","used","in","Poisson","sampling",".","kwargs","Other","parameters","passed","to","the","learning","node","."]
code: def __init__(
      self, stats, depth, splitter, drift_detector, rng, **kwargs
  ):
      super().__init__(stats, depth, splitter, **kwargs)
  
      self.drift_detector = drift_detector
      self.rng = rng
  
      self._error_tracker = st.Var()
  
imports: from __future__ import annotations
  import math
  from river import stats
  from river.utils.random import poisson
  from .branch import DTBranch, NominalBinaryBranch, NominalMultiwayBranch, NumericBinaryBranch, NumericMultiwayBranch
  from .htr_nodes import LeafAdaptive, LeafMean, LeafModel
  from .leaf import HTLeaf
label_desc: Use image node_id 1 to create a new AdaLeafRegressor object from inherited base classes: HTLeaf with example: obj = AdaLeafRegressor(stats, depth, splitter, drift_detector, rng)