class_docstr: RandomForestRegressor that supports conditional std computation.
  
  Parameters
  ----------
  n_estimators : integer, optional (default=10)
      The number of trees in the forest.
  
  criterion : string, optional (default="mse")
      The function to measure the quality of a split. Supported criteria
      are "mse" for the mean squared error, which is equal to variance
      reduction as feature selection criterion, and "mae" for the mean
      absolute error.
  
  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.
  
  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.
  
  min_weight_fraction_leaf : float, optional (default=0.)
      The minimum weighted fraction of the sum total of weights (of all
      the input samples) required to be at a leaf node. Samples have
      equal weight when sample_weight is not provided.
  
  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.
  
  min_impurity_decrease : float, optional (default=0.)
      A node will be split if this split induces a decrease of the impurity
      greater than or equal to this value.
      The weighted impurity decrease equation is the following::
  
          N_t / N * (impurity - N_t_R / N_t * right_impurity
                              - N_t_L / N_t * left_impurity)
  
      where ``N`` is the total number of samples, ``N_t`` is the number of
      samples at the current node, ``N_t_L`` is the number of samples in the
      left child, and ``N_t_R`` is the number of samples in the right child.
      ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
      if ``sample_weight`` is passed.
  
  bootstrap : boolean, optional (default=True)
      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 DecisionTreeRegressor
      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.
  
  Notes
  -----
  The default values for the parameters controlling the size of the trees
  (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
  unpruned trees which can potentially be very large on some data sets. To
  reduce memory consumption, the complexity and size of the trees should be
  controlled by setting those parameter values.
  The features are always randomly permuted at each split. Therefore,
  the best found split may vary, even with the same training data,
  ``max_features=n_features`` and ``bootstrap=False``, if the improvement
  of the criterion is identical for several splits enumerated during the
  search of the best split. To obtain a deterministic behaviour during
  fitting, ``random_state`` has to be fixed.
  
  References
  ----------
  .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
docstr: Predict continuous output for X.
  
  Parameters
  ----------
  X : array of shape = (n_samples, n_features)
      Input data.
  
  return_std : boolean
      Whether or not to return the standard deviation.
  
  Returns
  -------
  predictions : array-like of shape = (n_samples,)
      Predicted values for X. If criterion is set to "mse",
      then `predictions[i] ~= mean(y | X[i])`.
  
  std : array-like of shape=(n_samples,)
      Standard deviation of `y` at `X`. If criterion
      is set to "mse", then `std[i] ~= std(y | X[i])`.
code: def predict(self, X, return_std=False):
      """Predict continuous output for X.
  
      Parameters
      ----------
      X : array of shape = (n_samples, n_features)
          Input data.
  
      return_std : boolean
          Whether or not to return the standard deviation.
  
      Returns
      -------
      predictions : array-like of shape = (n_samples,)
          Predicted values for X. If criterion is set to "mse",
          then `predictions[i] ~= mean(y | X[i])`.
  
      std : array-like of shape=(n_samples,)
          Standard deviation of `y` at `X`. If criterion
          is set to "mse", then `std[i] ~= std(y | X[i])`.
  
      """
      mean = super(RandomForestRegressor, self).predict(X)
  
      if return_std:
          if self.criterion != "mse":
              raise ValueError(
                  "Expected impurity to be 'mse', got %s instead"
                  % self.criterion
              )
          std = _return_std(
              X, self.estimators_, mean, self.min_variance
          )
          return mean, std
      return mean
  
file_path: scikit-optimize/skopt/learning/forest.py
imports: import numpy
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.ensemble import ExtraTreesRegressor
label_desc: Use image node_id 2 for calling the RandomForestRegressor obj's underlying member method code with example usage: obj.predict(X, return_std) and returns: mean, mean,  std