class_docstr: Node that splits the data in a Hoeffding Adaptive Tree Regression.
  
  Parameters
  ----------
  stats
      Target stats.
  depth
      The depth of the node.
  drift_detector
      The detector used to monitor concept drifts.
  attributes
      Other parameters passed to the split node.
class_docstr_tok: ["Node","that","splits","the","data","in","a","Hoeffding","Adaptive","Tree","Regression",".","Parameters","--","--","--","--","--","stats","Target","stats",".","depth","The","depth","of","the","node",".","drift_detector","The","detector","used","to","monitor","concept","drifts",".","attributes","Other","parameters","passed","to","the","split","node","."]
code: def __init__(self, stats, *children, drift_detector, **attributes):
      stats = stats if stats else st.Var()
      super().__init__(stats, *children, **attributes)
      self.drift_detector = drift_detector
      self._alternate_tree = None
  
      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 AdaBranchRegressor object from inherited base classes: DTBranch with example: obj = AdaBranchRegressor(stats)