class_docstr: ARF Naive Bayes learning node 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.
      Number of attributes per subset for each node split.
  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","Naive","Bayes","learning","node","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",".","Number","of","attributes","per","subset","for","each","node","split",".","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 RandomLeafNaiveBayes object from inherited base classes: BaseRandomLeaf, LeafNaiveBayes with example: obj = RandomLeafNaiveBayes(stats, depth, splitter, max_features, rng)