class_docstr: Base class for the tree splitters.
  
  Each Attribute Observer (AO) or Splitter monitors one input feature and finds the best
  split point for this attribute. AOs can also perform other tasks related to the monitored
  feature, such as estimating its probability density function (classification case).
  
  This class should not be instantiated, as none of its methods are implemented.
class_docstr_tok: ["Base","class","for","the","tree","splitters",".","Each","Attribute","Observer","(","AO",")","or","Splitter","monitors","one","input","feature","and","finds","the","best","split","point","for","this","attribute",".","AOs","can","also","perform","other","tasks","related","to","the","monitored","feature",",","such","as","estimating","its","probability","density","function","(","classification","case",")",".","This","class","should","not","be","instantiated",",","as","none","of","its","methods","are","implemented","."]
docstr: Check on which kind of learning task the splitter is designed to work.
  
  If `True`, the splitter works with classification trees, otherwise it is designed for
  regression trees.
docstr_tok: ["Check","on","which","kind","of","learning","task","the","splitter","is","designed","to","work",".","If","`","True","`",",","the","splitter","works","with","classification","trees",",","otherwise","it","is","designed","for","regression","trees","."]
code: def is_target_class(self) -> bool:
      """Check on which kind of learning task the splitter is designed to work.
  
      If `True`, the splitter works with classification trees, otherwise it is designed for
      regression trees.
      """
      return True
  
file_path: river/river/tree/splitter/base.py
imports: from __future__ import annotations
  import abc
  import typing
  from river import base
  from ..split_criterion.base import SplitCriterion
  from ..utils import BranchFactory, GradHess, GradHessStats
label_desc: Use image node_id 5 for calling the Splitter obj's underlying member method code with example usage: obj.is_target_class() and returns: True