class_docstr: A Bagging regressor.
  
  A Bagging regressor is an ensemble meta-estimator that fits base
  regressors each on random subsets of the original dataset and then
  aggregate their individual predictions (either by voting or by averaging)
  to form a final prediction. Such a meta-estimator can typically be used as
  a way to reduce the variance of a black-box estimator (e.g., a decision
  tree), by introducing randomization into its construction procedure and
  then making an ensemble out of it.
  
  This algorithm encompasses several works from the literature. When random
  subsets of the dataset are drawn as random subsets of the samples, then
  this algorithm is known as Pasting [1]_. If samples are drawn with
  replacement, then the method is known as Bagging [2]_. When random subsets
  of the dataset are drawn as random subsets of the features, then the method
  is known as Random Subspaces [3]_. Finally, when base estimators are built
  on subsets of both samples and features, then the method is known as
  Random Patches [4]_.
  
  Read more in the :ref:`User Guide <bagging>`.
  
  .. versionadded:: 0.15
  
  Parameters
  ----------
  estimator : object, default=None
      The base estimator to fit on random subsets of the dataset.
      If None, then the base estimator is a
      :class:`~sklearn.tree.DecisionTreeRegressor`.
  
      .. versionadded:: 1.2
         `base_estimator` was renamed to `estimator`.
  
  n_estimators : int, default=10
      The number of base estimators in the ensemble.
  
  max_samples : int or float, default=1.0
      The number of samples to draw from X to train each base estimator (with
      replacement by default, see `bootstrap` for more details).
  
      - If int, then draw `max_samples` samples.
      - If float, then draw `max_samples * X.shape[0]` samples.
  
  max_features : int or float, default=1.0
      The number of features to draw from X to train each base estimator (
      without replacement by default, see `bootstrap_features` for more
      details).
  
      - If int, then draw `max_features` features.
      - If float, then draw `max(1, int(max_features * n_features_in_))` features.
  
  bootstrap : bool, default=True
      Whether samples are drawn with replacement. If False, sampling
      without replacement is performed.
  
  bootstrap_features : bool, default=False
      Whether features are drawn with replacement.
  
  oob_score : bool, default=False
      Whether to use out-of-bag samples to estimate
      the generalization error. Only available if bootstrap=True.
  
  warm_start : bool, 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 ensemble. See :term:`the Glossary <warm_start>`.
  
  n_jobs : int, default=None
      The number of jobs to run in parallel for both :meth:`fit` and
      :meth:`predict`. ``None`` means 1 unless in a
      :obj:`joblib.parallel_backend` context. ``-1`` means using all
      processors. See :term:`Glossary <n_jobs>` for more details.
  
  random_state : int, RandomState instance or None, default=None
      Controls the random resampling of the original dataset
      (sample wise and feature wise).
      If the base estimator accepts a `random_state` attribute, a different
      seed is generated for each instance in the ensemble.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  verbose : int, default=0
      Controls the verbosity when fitting and predicting.
  
  base_estimator : object, default="deprecated"
      Use `estimator` instead.
  
      .. deprecated:: 1.2
          `base_estimator` is deprecated and will be removed in 1.4.
          Use `estimator` instead.
  
  Attributes
  ----------
  estimator_ : estimator
      The base estimator from which the ensemble is grown.
  
      .. versionadded:: 1.2
         `base_estimator_` was renamed to `estimator_`.
  
  base_estimator_ : estimator
      The base estimator from which the ensemble is grown.
  
      .. deprecated:: 1.2
          `base_estimator_` is deprecated and will be removed in 1.4.
          Use `estimator_` instead.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  estimators_ : list of estimators
      The collection of fitted sub-estimators.
  
  estimators_samples_ : list of arrays
      The subset of drawn samples (i.e., the in-bag samples) for each base
      estimator. Each subset is defined by an array of the indices selected.
  
  estimators_features_ : list of arrays
      The subset of drawn features for each base estimator.
  
  oob_score_ : float
      Score of the training dataset obtained using an out-of-bag estimate.
      This attribute exists only when ``oob_score`` is True.
  
  oob_prediction_ : ndarray of shape (n_samples,)
      Prediction computed with out-of-bag estimate on the training
      set. If n_estimators is small it might be possible that a data point
      was never left out during the bootstrap. In this case,
      `oob_prediction_` might contain NaN. This attribute exists only
      when ``oob_score`` is True.
  
  See Also
  --------
  BaggingClassifier : A Bagging classifier.
  
  References
  ----------
  
  .. [1] L. Breiman, "Pasting small votes for classification in large
         databases and on-line", Machine Learning, 36(1), 85-103, 1999.
  
  .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123-140,
         1996.
  
  .. [3] T. Ho, "The random subspace method for constructing decision
         forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844,
         1998.
  
  .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine
         Learning and Knowledge Discovery in Databases, 346-361, 2012.
  
  Examples
  --------
  >>> from sklearn.svm import SVR
  >>> from sklearn.ensemble import BaggingRegressor
  >>> from sklearn.datasets import make_regression
  >>> X, y = make_regression(n_samples=100, n_features=4,
  ...                        n_informative=2, n_targets=1,
  ...                        random_state=0, shuffle=False)
  >>> regr = BaggingRegressor(estimator=SVR(),
  ...                         n_estimators=10, random_state=0).fit(X, y)
  >>> regr.predict([[0, 0, 0, 0]])
  array([-2.8720...])
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docstr: Predict regression target for X.
  
  The predicted regression target of an input sample is computed as the
  mean predicted regression targets of the estimators in the ensemble.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The training input samples. Sparse matrices are accepted only if
      they are supported by the base estimator.
  
  Returns
  -------
  y : ndarray of shape (n_samples,)
      The predicted values.
docstr_tok: ["Predict","regression","target","for","X",".","The","predicted","regression","target","of","an","input","sample","is","computed","as","the","mean","predicted","regression","targets","of","the","estimators","in","the","ensemble",".","Parameters","--","--","--","--","--","X",":","{","array-like",",","sparse","matrix","}","of","shape","(","n_samples",",","n_features",")","The","training","input","samples",".","Sparse","matrices","are","accepted","only","if","they","are","supported","by","the","base","estimator",".","Returns","--","--","--","-","y",":","ndarray","of","shape","(","n_samples",",",")","The","predicted","values","."]
code: def predict(self, X):
      """Predict regression target for X.
  
      The predicted regression target of an input sample is computed as the
      mean predicted regression targets of the estimators in the ensemble.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The training input samples. Sparse matrices are accepted only if
          they are supported by the base estimator.
  
      Returns
      -------
      y : ndarray of shape (n_samples,)
          The predicted values.
      """
      check_is_fitted(self)
      # Check data
      X = self._validate_data(
          X,
          accept_sparse=["csr", "csc"],
          dtype=None,
          force_all_finite=False,
          reset=False,
      )
  
      # Parallel loop
      n_jobs, _, starts = _partition_estimators(
          self.n_estimators, self.n_jobs
      )
  
      all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
          delayed(_parallel_predict_regression)(
              self.estimators_[starts[i] : starts[i + 1]],
              self.estimators_features_[starts[i] : starts[i + 1]],
              X,
          )
          for i in range(n_jobs)
      )
  
      # Reduce
      y_hat = sum(all_y_hat) / self.n_estimators
  
      return y_hat
  
imports: import itertools
  import numbers
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from numbers import Integral
  from warnings import warn
  import numpy
  from ..base import ClassifierMixin, RegressorMixin, _fit_context
  from ..metrics import accuracy_score, r2_score
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..utils import check_random_state, column_or_1d, indices_to_mask
  from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
  from ..utils._tags import _safe_tags
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import check_classification_targets
  from ..utils.parallel import Parallel, delayed
  from ..utils.random import sample_without_replacement
  from ..utils.validation import _check_sample_weight, check_is_fitted, has_fit_parameter
  from ._base import BaseEnsemble, _partition_estimators
label_desc: Use image node_id 2 for calling the BaggingRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: y_hat