class_docstr: Variance Reduction split criterion.
  
  Often employed in cases where the target variable is continuous (regression tree),
  meaning that use of many other metrics would first require discretization before being applied.
  
  References
  ----------
  [Wikipedia entry](https://en.wikipedia.org/wiki/Decision_tree_learning#Variance_reduction)
code: def range_of_merit(pre_split_dist):
      # The VR values are unbounded, but as we compare the ratio between the attributes' VRs
      # the actual range is between 0 (the second best candidate has a merit of zero) and 1
      # (both compared split candidates have the same merit).
      return 1.0
  
file_path: river/river/tree/split_criterion/variance_reduction_split_criterion.py
imports: from __future__ import annotations
  from .base import SplitCriterion
label_desc: Use image node_id 4 for calling the VarianceReductionSplitCriterion obj's underlying member method code with example usage: obj.range_of_merit(pre_split_dist) and returns: int