class_docstr: Hoeffding Tree regressor.
  
  Parameters
  ----------
  grace_period
      Number of instances a leaf should observe between split attempts.
  max_depth
      The maximum depth a tree can reach. If `None`, the tree will grow indefinitely.
  delta
      Significance level to calculate the Hoeffding bound. The significance level is given by
      `1 - delta`. Values closer to zero imply longer split decision delays.
  tau
      Threshold below which a split will be forced to break ties.
  leaf_prediction
      Prediction mechanism used at leafs.</br>
      - 'mean' - Target mean</br>
      - 'model' - Uses the model defined in `leaf_model`</br>
      - 'adaptive' - Chooses between 'mean' and 'model' dynamically</br>
  leaf_model
      The regression model used to provide responses if `leaf_prediction='model'`. If not
      provided an instance of `river.linear_model.LinearRegression` with the default
      hyperparameters is used.
  model_selector_decay
      The exponential decaying factor applied to the learning models' squared errors, that
      are monitored if `leaf_prediction='adaptive'`. Must be between `0` and `1`. The closer
      to `1`, the more importance is going to be given to past observations. On the other hand,
      if its value approaches `0`, the recent observed errors are going to have more influence
      on the final decision.
  nominal_attributes
      List of Nominal attributes identifiers. If empty, then assume that all numeric attributes
      should be treated as continuous.
  splitter
      The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric
      features and perform splits. Splitters are available in the `tree.splitter` module.
      Different splitters are available for classification and regression tasks. Classification
      and regression splitters can be distinguished by their property `is_target_class`.
      This is an advanced option. Special care must be taken when choosing different splitters.
      By default, `tree.splitter.TEBSTSplitter` is used if `splitter` is `None`.
  min_samples_split
      The minimum number of samples every branch resulting from a split candidate must have
      to be considered valid.
  binary_split
      If True, only allow binary splits.
  max_size
      The max size of the tree, in Megabytes (MB).
  memory_estimate_period
      Interval (number of processed instances) between memory consumption checks.
  stop_mem_management
      If True, stop growing as soon as memory limit is hit.
  remove_poor_attrs
      If True, disable poor attributes to reduce memory usage.
  merit_preprune
      If True, enable merit-based tree pre-pruning.
  
  Notes
  -----
  The Hoeffding Tree Regressor (HTR) is an adaptation of the incremental tree algorithm of the
  same name for classification. Similarly to its classification counterpart, HTR uses the
  Hoeffding bound to control its split decisions. Differently from the classification algorithm,
  HTR relies on calculating the reduction of variance in the target space to decide among the
  split candidates. The smallest the variance at its leaf nodes, the more homogeneous the
  partitions are. At its leaf nodes, HTR fits either linear models or uses the target
  average as the predictor.
  
  Examples
  --------
  >>> from river import datasets
  >>> from river import evaluate
  >>> from river import metrics
  >>> from river import tree
  >>> from river import preprocessing
  
  >>> dataset = datasets.TrumpApproval()
  
  >>> model = (
  ...     preprocessing.StandardScaler() |
  ...     tree.HoeffdingTreeRegressor(
  ...         grace_period=100,
  ...         model_selector_decay=0.9
  ...     )
  ... )
  
  >>> metric = metrics.MAE()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  MAE: 0.793345
class_docstr_tok: ["Hoeffding","Tree","regressor",".","Parameters","--","--","--","--","--","grace_period","Number","of","instances","a","leaf","should","observe","between","split","attempts",".","max_depth","The","maximum","depth","a","tree","can","reach",".","If","`","None","`",",","the","tree","will","grow","indefinitely",".","delta","Significance","level","to","calculate","the","Hoeffding","bound",".","The","significance","level","is","given","by","`","1","-","delta","`",".","Values","closer","to","zero","imply","longer","split","decision","delays",".","tau","Threshold","below","which","a","split","will","be","forced","to","break","ties",".","leaf_prediction","Prediction","mechanism","used","at","leafs.","<","\/br",">","-","'mean","'","-","Target","mean","<","\/br",">","-","'model","'","-","Uses","the","model","defined","in","`","leaf_model","`","<","\/br",">","-","'adaptive","'","-","Chooses","between","'mean","'","and","'model","'","dynamically","<","\/br",">","leaf_model","The","regression","model","used","to","provide","responses","if","`","leaf_prediction='model","'","`",".","If","not","provided","an","instance","of","`","river.linear_model.LinearRegression","`","with","the","default","hyperparameters","is","used",".","model_selector_decay","The","exponential","decaying","factor","applied","to","the","learning","models","'","squared","errors",",","that","are","monitored","if","`","leaf_prediction='adaptive","'","`",".","Must","be","between","`","0","`","and","`","1","`",".","The","closer","to","`","1","`",",","the","more","importance","is","going","to","be","given","to","past","observations",".","On","the","other","hand",",","if","its","value","approaches","`","0","`",",","the","recent","observed","errors","are","going","to","have","more","influence","on","the","final","decision",".","nominal_attributes","List","of","Nominal","attributes","identifiers",".","If","empty",",","then","assume","that","all","numeric","attributes","should","be","treated","as","continuous",".","splitter","The","Splitter","or","Attribute","Observer","(","AO",")","used","to","monitor","the","class","statistics","of","numeric","features","and","perform","splits",".","Splitters","are","available","in","the","`","tree.splitter","`","module",".","Different","splitters","are","available","for","classification","and","regression","tasks",".","Classification","and","regression","splitters","can","be","distinguished","by","their","property","`","is_target_class","`",".","This","is","an","advanced","option",".","Special","care","must","be","taken","when","choosing","different","splitters",".","By","default",",","`","tree.splitter.TEBSTSplitter","`","is","used","if","`","splitter","`","is","`","None","`",".","min_samples_split","The","minimum","number","of","samples","every","branch","resulting","from","a","split","candidate","must","have","to","be","considered","valid",".","binary_split","If","True",",","only","allow","binary","splits",".","max_size","The","max","size","of","the","tree",",","in","Megabytes","(","MB",")",".","memory_estimate_period","Interval","(","number","of","processed","instances",")","between","memory","consumption","checks",".","stop_mem_management","If","True",",","stop","growing","as","soon","as","memory","limit","is","hit",".","remove_poor_attrs","If","True",",","disable","poor","attributes","to","reduce","memory","usage",".","merit_preprune","If","True",",","enable","merit-based","tree","pre-pruning",".","Notes","--","--","-","The","Hoeffding","Tree","Regressor","(","HTR",")","is","an","adaptation","of","the","incremental","tree","algorithm","of","the","same","name","for","classification",".","Similarly","to","its","classification","counterpart",",","HTR","uses","the","Hoeffding","bound","to","control","its","split","decisions",".","Differently","from","the","classification","algorithm",",","HTR","relies","on","calculating","the","reduction","of","variance","in","the","target","space","to","decide","among","the","split","candidates",".","The","smallest","the","variance","at","its","leaf","nodes",",","the","more","homogeneous","the","partitions","are",".","At","its","leaf","nodes",",","HTR","fits","either","linear","models","or","uses","the","target","average","as","the","predictor",".","Examples","--","--","--","--",">",">",">","from","river","import","datasets",">",">",">","from","river","import","evaluate",">",">",">","from","river","import","metrics",">",">",">","from","river","import","tree",">",">",">","from","river","import","preprocessing",">",">",">","dataset","=","datasets.TrumpApproval","(",")",">",">",">","model","=","(","...","preprocessing.StandardScaler","(",")","|","...","tree.HoeffdingTreeRegressor","(","...","grace_period=100",",","...","model_selector_decay=0.9","...",")","...",")",">",">",">","metric","=","metrics.MAE","(",")",">",">",">","evaluate.progressive_val_score","(","dataset",",","model",",","metric",")","MAE",":","0.793345"]
code: def _new_split_criterion(self):
      return VarianceReductionSplitCriterion(
          min_samples_split=self.min_samples_split
      )
  
imports: from __future__ import annotations
  from copy import deepcopy
  from river import base, linear_model
  from .hoeffding_tree import HoeffdingTree
  from .nodes.branch import DTBranch
  from .nodes.htr_nodes import LeafAdaptive, LeafMean, LeafModel
  from .nodes.leaf import HTLeaf
  from .split_criterion import VarianceReductionSplitCriterion
  from .splitter import Splitter, TEBSTSplitter
label_desc: Use image node_id 5 for calling the HoeffdingTreeRegressor obj's underlying member method code with example usage: obj._new_split_criterion() and returns: VarianceReductionSplitCriterion