class_docstr: ARF learning node for regression tasks that dynamically selects between the
  target mean and the output of a learning model to provide responses.
  
  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.
  leaf_model
      A `base.Regressor` instance used to learn from instances and provide
      responses.
  kwargs
      Other parameters passed to the learning node.
class_docstr_tok: ["ARF","learning","node","for","regression","tasks","that","dynamically","selects","between","the","target","mean","and","the","output","of","a","learning","model","to","provide","responses",".","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",".","leaf_model","A","`","base.Regressor","`","instance","used","to","learn","from","instances","and","provide","responses",".","kwargs","Other","parameters","passed","to","the","learning","node","."]
code: def __init__(
      self,
      stats,
      depth,
      splitter,
      max_features,
      rng,
      leaf_model,
      **kwargs,
  ):
      super().__init__(
          stats,
          depth,
          splitter,
          max_features,
          rng,
          leaf_model=leaf_model,
          **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 RandomLeafAdaptive object from inherited base classes: BaseRandomLeaf, LeafAdaptive with example: obj = RandomLeafAdaptive(stats, depth, splitter, max_features, rng, leaf_model)