class_docstr: A decision tree regressor that provides quantile estimates.
  
  Parameters
  ----------
  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.
  
  splitter : string, optional (default="best")
      The strategy used to choose the split at each node. Supported
      strategies are "best" to choose the best split and "random" to choose
      the best random split.
  
  max_features : int, float, string or None, optional (default=None)
      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 : int 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 a tree 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.
  
  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`.
  
  presort : bool, optional (default=False)
      Whether to presort the data to speed up the finding of best splits in
      fitting. For the default settings of a decision tree on large
      datasets, setting this to true may slow down the training process.
      When using either a smaller dataset or a restricted depth, this may
      speed up the training.
  
  Attributes
  ----------
  feature_importances_ : array of shape = [n_features]
      The feature importances.
      The higher, the more important the feature.
      The importance of a feature is computed as the
      (normalized) total reduction of the criterion brought
      by that feature. It is also known as the Gini importance [4]_.
  
  max_features_ : int,
      The inferred value of max_features.
  
  n_features_ : int
      The number of features when ``fit`` is performed.
  
  n_outputs_ : int
      The number of outputs when ``fit`` is performed.
  
  tree_ : Tree object
      The underlying Tree object.
  
  y_train_ : array-like
      Train target values.
  
  y_train_leaves_ : array-like.
      Cache the leaf nodes that each training sample falls into.
      y_train_leaves_[i] is the leaf that y_train[i] ends up at.
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code: def __init__(
      self,
      criterion="squared_error",
      splitter="best",
      max_depth=None,
      min_samples_split=2,
      min_samples_leaf=1,
      max_features=None,
      random_state=None,
      max_leaf_nodes=None,
  ):
      super(DecisionTreeQuantileRegressor, self).__init__(
          criterion=criterion,
          splitter=splitter,
          max_depth=max_depth,
          min_samples_split=min_samples_split,
          min_samples_leaf=min_samples_leaf,
          max_features=max_features,
          max_leaf_nodes=max_leaf_nodes,
          random_state=random_state,
      )
  
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 DecisionTreeQuantileRegressor object from inherited base classes: BaseTreeQuantileRegressor, DecisionTreeRegressor with example: obj = DecisionTreeQuantileRegressor(criterion, splitter, max_depth, min_samples_split, min_samples_leaf, max_features, random_state, max_leaf_nodes)