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_binary_split(self, val_idx):
      equal_dist = {}
      not_equal_dist = {}
      for class_val, att_dist in self._att_dist_per_class.items():
          for att_val, weight in att_dist.items():
              if att_val == val_idx:
                  if class_val not in equal_dist:
                      equal_dist[class_val] = 0.0
                  equal_dist[class_val] += weight
              else:
                  if class_val not in not_equal_dist:
                      not_equal_dist[class_val] = 0.0
                  not_equal_dist[class_val] += weight
      return [equal_dist, not_equal_dist]
  
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 7 for calling the NominalSplitterClassif obj's underlying member method code with example usage: obj._class_dist_from_binary_split(val_idx) and returns: list