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.
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docstr: Update statistics of this observer given an attribute value, its target value
  and the weight of the instance observed.
  
  Parameters
  ----------
  att_val
      The value of the monitored attribute.
  target_val
      The target value.
  w
      The weight of the instance.
docstr_tok: ["Update","statistics","of","this","observer","given","an","attribute","value",",","its","target","value","and","the","weight","of","the","instance","observed",".","Parameters","--","--","--","--","--","att_val","The","value","of","the","monitored","attribute",".","target_val","The","target","value",".","w","The","weight","of","the","instance","."]
code: def update(self, att_val, target_val: base.typing.Target, w: float):
      """Update statistics of this observer given an attribute value, its target value
      and the weight of the instance observed.
  
      Parameters
      ----------
      att_val
          The value of the monitored attribute.
      target_val
          The target value.
      w
          The weight of the instance.
  
      """
  
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 1 for calling the Splitter obj's underlying member method code with example usage: obj.update(att_val, target_val, w) without return types