class_docstr: An extra-trees regressor that provides quantile estimates.
  This class implements a meta estimator that fits a number of
  randomized decision trees (a.k.a. extra-trees) on various sub-samples
  of the dataset and use averaging to improve the predictive accuracy
  and control over-fitting.
  
  Parameters
  ----------
  n_estimators : integer, optional (default=10)
      The number of trees in the forest.
  criterion : string, optional (default="squared_error")
      The function to measure the quality of a split. Supported criteria
      are "squared_error" for the mean squared error, which is equal to variance
      reduction as feature selection criterion, and "mae" for the mean
      absolute error.
      .. versionadded:: 0.18
         Mean Absolute Error (MAE) criterion.
  max_features : int, float, string or None, optional (default="auto")
      The number of features to consider when looking for the best split:
      - If int, then consider `max_features` features at each split.
      - If float, then `max_features` is a percentage and
        `int(max_features * n_features)` features are considered at each
        split.
      - If "auto", then `max_features=n_features`.
      - If "sqrt", then `max_features=sqrt(n_features)`.
      - If "log2", then `max_features=log2(n_features)`.
      - If None, then `max_features=n_features`.
      Note: the search for a split does not stop until at least one
      valid partition of the node samples is found, even if it requires to
      effectively inspect more than ``max_features`` features.
  max_depth : integer or None, optional (default=None)
      The maximum depth of the tree. If None, then nodes are expanded until
      all leaves are pure or until all leaves contain less than
      min_samples_split samples.
  min_samples_split : int, float, optional (default=2)
      The minimum number of samples required to split an internal node:
      - If int, then consider `min_samples_split` as the minimum number.
      - If float, then `min_samples_split` is a percentage and
        `ceil(min_samples_split * n_samples)` are the minimum
        number of samples for each split.
      .. versionchanged:: 0.18
         Added float values for percentages.
  min_samples_leaf : int, float, optional (default=1)
      The minimum number of samples required to be at a leaf node:
      - If int, then consider `min_samples_leaf` as the minimum number.
      - If float, then `min_samples_leaf` is a percentage and
        `ceil(min_samples_leaf * n_samples)` are the minimum
        number of samples for each node.
      .. versionchanged:: 0.18
         Added float values for percentages.
  max_leaf_nodes : int or None, optional (default=None)
      Grow trees with ``max_leaf_nodes`` in best-first fashion.
      Best nodes are defined as relative reduction in impurity.
      If None then unlimited number of leaf nodes.
  bootstrap : boolean, optional (default=False)
      Whether bootstrap samples are used when building trees.
  oob_score : bool, optional (default=False)
      Whether to use out-of-bag samples to estimate the R^2 on unseen data.
  n_jobs : integer, optional (default=1)
      The number of jobs to run in parallel for both `fit` and `predict`.
      If -1, then the number of jobs is set to the number of cores.
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  verbose : int, optional (default=0)
      Controls the verbosity of the tree building process.
  warm_start : bool, optional (default=False)
      When set to ``True``, reuse the solution of the previous call to fit
      and add more estimators to the ensemble, otherwise, just fit a whole
      new forest.
  
  Attributes
  ----------
  estimators_ : list of ExtraTreeQuantileRegressor
      The collection of fitted sub-estimators.
  feature_importances_ : array of shape = [n_features]
      The feature importances (the higher, the more important the feature).
  n_features_ : int
      The number of features when ``fit`` is performed.
  n_outputs_ : int
      The number of outputs when ``fit`` is performed.
  oob_score_ : float
      Score of the training dataset obtained using an out-of-bag estimate.
  oob_prediction_ : array of shape = [n_samples]
      Prediction computed with out-of-bag estimate on the training set.
  y_train_ : array-like, shape=(n_samples,)
      Cache the target values at fit time.
  y_weights_ : array-like, shape=(n_estimators, n_samples)
      y_weights_[i, j] is the weight given to sample ``j` while
      estimator ``i`` is fit. If bootstrap is set to True, this
      reduces to a 2-D array of ones.
  y_train_leaves_ : array-like, shape=(n_estimators, n_samples)
      y_train_leaves_[i, j] provides the leaf node that y_train_[i]
      ends up when estimator j is fit. If y_train_[i] is given
      a weight of zero when estimator j is fit, then the value is -1.
  
  References
  ----------
  .. [1] Nicolai Meinshausen, Quantile Regression Forests
      http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf
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code: def __init__(
      self,
      n_estimators=10,
      criterion="squared_error",
      max_depth=None,
      min_samples_split=2,
      min_samples_leaf=1,
      max_features=1.0,
      max_leaf_nodes=None,
      bootstrap=True,
      oob_score=False,
      n_jobs=1,
      random_state=None,
      verbose=0,
      warm_start=False,
      max_samples=None,
  ):
      super(ExtraTreesQuantileRegressor, self).__init__(
          ExtraTreeQuantileRegressor(),
          n_estimators=n_estimators,
          estimator_params=(
              "criterion",
              "max_depth",
              "min_samples_split",
              "min_samples_leaf",
              "max_features",
              "max_leaf_nodes",
              "random_state",
          ),
          bootstrap=bootstrap,
          oob_score=oob_score,
          n_jobs=n_jobs,
          random_state=random_state,
          verbose=verbose,
          warm_start=warm_start,
          max_samples=max_samples,
      )
  
      self.criterion = criterion
      self.max_depth = max_depth
      self.min_samples_split = min_samples_split
      self.min_samples_leaf = min_samples_leaf
      self.max_features = max_features
      self.max_leaf_nodes = max_leaf_nodes
  
imports: import logging
  from functools import partial
  import numpy
  import pandas
  from sklearn.ensemble._forest import ForestRegressor
  from sklearn.tree import BaseDecisionTree, DecisionTreeRegressor, ExtraTreeRegressor
  from sklearn.utils import check_array, check_random_state, check_X_y
label_desc: Use image node_id 1 to create a new ExtraTreesQuantileRegressor object from inherited base classes: BaseForestQuantileRegressor with example: obj = ExtraTreesQuantileRegressor(n_estimators, criterion, max_depth, min_samples_split, min_samples_leaf, max_features, max_leaf_nodes, bootstrap, oob_score, n_jobs, random_state, verbose, warm_start, max_samples)