class_docstr: 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.
  kwargs
      Other parameters passed to the learning node.
class_docstr_tok: ["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",".","kwargs","Other","parameters","passed","to","the","learning","node","."]
code: def new_nominal_splitter():
      return NominalSplitterReg()
  
imports: from __future__ import annotations
  import inspect
  from river.stats import Var
  from ..splitter import EBSTSplitter
  from ..splitter.nominal_splitter_reg import NominalSplitterReg
  from .leaf import HTLeaf
label_desc: Use image node_id 2 for calling the LeafMean obj's underlying member method code with example usage: obj.new_nominal_splitter() and returns: NominalSplitterReg