class_docstr: ARF learning Node for regression tasks that always use the average target
  value as response.
  
  Parameters
  ----------
  stats
      In regression tasks the node keeps an instance of `river.stats.Var` to estimate
      the target's statistics.
  depth
      The depth of the node.
  splitter
      The numeric attribute observer algorithm used to monitor target statistics
      and perform split attempts.
  max_features
      Number of attributes per subset for each node split.
  rng
      Random number generator.
  kwargs
      Other parameters passed to the learning node.
class_docstr_tok: ["ARF","learning","Node","for","regression","tasks","that","always","use","the","average","target","value","as","response",".","Parameters","--","--","--","--","--","stats","In","regression","tasks","the","node","keeps","an","instance","of","`","river.stats.Var","`","to","estimate","the","target","'s","statistics",".","depth","The","depth","of","the","node",".","splitter","The","numeric","attribute","observer","algorithm","used","to","monitor","target","statistics","and","perform","split","attempts",".","max_features","Number","of","attributes","per","subset","for","each","node","split",".","rng","Random","number","generator",".","kwargs","Other","parameters","passed","to","the","learning","node","."]
code: def __init__(
      self, stats, depth, splitter, max_features, rng, **kwargs
  ):
      super().__init__(
          stats, depth, splitter, max_features, rng, **kwargs
      )
  
file_path: river/river/tree/nodes/arf_htr_nodes.py
imports: from __future__ import annotations
  from .arf_htc_nodes import BaseRandomLeaf
  from .htr_nodes import LeafAdaptive, LeafMean, LeafModel
label_desc: Use image node_id 1 to create a new RandomLeafMean object from inherited base classes: BaseRandomLeaf, LeafMean with example: obj = RandomLeafMean(stats, depth, splitter, max_features, rng)