class_docstr: Splitter utilized to monitor nominal features in classification tasks.
  
  As the monitored feature is nominal, it already has well-defined partitions. Hence,
  this splitter uses dictionary structures to keep class counts for each incoming category.
class_docstr_tok: ["Splitter","utilized","to","monitor","nominal","features","in","classification","tasks",".","As","the","monitored","feature","is","nominal",",","it","already","has","well-defined","partitions",".","Hence",",","this","splitter","uses","dictionary","structures","to","keep","class","counts","for","each","incoming","category","."]
code: def _class_dist_from_multiway_split(self):
      resulting_dist = {}
      for class_val, att_dist in self._att_dist_per_class.items():
          for att_val, weight in att_dist.items():
              if att_val not in resulting_dist:
                  resulting_dist[att_val] = {}
              if class_val not in resulting_dist[att_val]:
                  resulting_dist[att_val][class_val] = 0.0
              resulting_dist[att_val][class_val] += weight
  
      sorted_keys = sorted(resulting_dist.keys())
      distributions = [
          dict(sorted(resulting_dist[k].items())) for k in sorted_keys
      ]
      return distributions
  
file_path: river/river/tree/splitter/nominal_splitter_classif.py
imports: from __future__ import annotations
  import collections
  from ..utils import BranchFactory
  from .base import Splitter
label_desc: Use image node_id 6 for calling the NominalSplitterClassif obj's underlying member method code with example usage: obj._class_dist_from_multiway_split() and returns: distributions