class_docstr: ARF learning node that always predicts the majority class.
  
  Parameters
  ----------
  stats
      Initial class observations.
  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","that","always","predicts","the","majority","class",".","Parameters","--","--","--","--","--","stats","Initial","class","observations",".","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
      )
  
imports: from __future__ import annotations
  import typing
  from .htc_nodes import LeafMajorityClass, LeafNaiveBayes, LeafNaiveBayesAdaptive
  from .leaf import HTLeaf
label_desc: Use image node_id 1 to create a new RandomLeafMajorityClass object from inherited base classes: BaseRandomLeaf, LeafMajorityClass with example: obj = RandomLeafMajorityClass(stats, depth, splitter, max_features, rng)