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 is_numeric(self):
      return False
  
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 2 for calling the NominalSplitterClassif obj's underlying member method code with example usage: obj.is_numeric() and returns: False