class_docstr: Splitter utilized to monitor nominal features in regression tasks.
  
  As the monitored feature is nominal, it already has well-defined partitions. Hence,
  this splitter simply keeps the split-enabling target statistics for each incoming
  category.
class_docstr_tok: ["Splitter","utilized","to","monitor","nominal","features","in","regression","tasks",".","As","the","monitored","feature","is","nominal",",","it","already","has","well-defined","partitions",".","Hence",",","this","splitter","simply","keeps","the","split-enabling","target","statistics","for","each","incoming","category","."]
code: def __init__(self):
      super().__init__()
      self._statistics = {}
      # The way in which the target statistics are update are selected on the fly.
      # Different strategies are used for univariate and multivariate regression.
      self._update_estimator = self._update_estimator_univariate
  
file_path: river/river/tree/splitter/nominal_splitter_reg.py
imports: from __future__ import annotations
  import functools
  from river.stats import Var
  from river.utils import VectorDict
  from ..utils import BranchFactory
  from .base import Splitter
label_desc: Use image node_id 1 to create a new NominalSplitterReg object from inherited base classes: Splitter with example: obj = NominalSplitterReg()