class_docstr: Extra Tree regressor.
  
  This is the base-estimator of the Extra Trees regressor.
  This variant of the Hoeffding Tree regressor includes the `max_features` parameter,
  which defines the number of randomly selected features to be considered at each split.
  It also evaluates split candidates randomly.
class_docstr_tok: ["Extra","Tree","regressor",".","This","is","the","base-estimator","of","the","Extra","Trees","regressor",".","This","variant","of","the","Hoeffding","Tree","regressor","includes","the","`","max_features","`","parameter",",","which","defines","the","number","of","randomly","selected","features","to","be","considered","at","each","split",".","It","also","evaluates","split","candidates","randomly","."]
code: def __init__(
      self,
      max_features,
      grace_period,
      max_depth,
      delta,
      tau,
      leaf_prediction,
      leaf_model,
      model_selector_decay,
      nominal_attributes,
      min_samples_split,
      binary_split,
      max_size,
      memory_estimate_period,
      stop_mem_management,
      remove_poor_attrs,
      merit_preprune,
      split_buffer_size,
      seed,
  ):
      self.max_features = max_features
      self.split_buffer_size = split_buffer_size
      self.seed = seed
      self._rng = random.Random(self.seed)
  
      super().__init__(
          grace_period=grace_period,
          max_depth=max_depth,
          delta=delta,
          tau=tau,
          leaf_prediction=leaf_prediction,
          leaf_model=leaf_model,
          model_selector_decay=model_selector_decay,
          nominal_attributes=nominal_attributes,
          splitter=RegRandomSplitter(
              seed=self._rng.randint(0, sys.maxsize),
              buffer_size=self.split_buffer_size,
          ),
          min_samples_split=min_samples_split,
          binary_split=binary_split,
          max_size=max_size,
          memory_estimate_period=memory_estimate_period,
          stop_mem_management=stop_mem_management,
          remove_poor_attrs=remove_poor_attrs,
          merit_preprune=merit_preprune,
      )
  
imports: from __future__ import annotations
  import abc
  import collections
  import math
  import random
  import sys
  from river import base, drift, metrics, tree, utils
  from river.tree.nodes.et_nodes import ETLeafAdaptive, ETLeafMean, ETLeafModel
  from river.tree.splitter.random_splitter import RegRandomSplitter
label_desc: Use image node_id 1 to create a new ETRegressor object from inherited base classes: tree with example: obj = ETRegressor(max_features, grace_period, max_depth, delta, tau, leaf_prediction, leaf_model, model_selector_decay, nominal_attributes, min_samples_split, binary_split, max_size, memory_estimate_period, stop_mem_management, remove_poor_attrs, merit_preprune, split_buffer_size, seed)