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# `sklearn.ensemble`.BaggingRegressor

class sklearn.ensemble.BaggingRegressor(estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0, base_estimator='deprecated')

  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 User Guide.

  New in version 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 `DecisionTreeRegressor`.

      New in version 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 the Glossary.

    n_jobs int, default=None

      The number of jobs to run in parallel for both `fit` and `predict`. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary 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 Glossary.

    verbose int, default=0

      Controls the verbosity when fitting and predicting.

    base_estimator object, default=”deprecated”

      Use `estimator` instead.

      Deprecated since version 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.

      New in version 1.2: `base_estimator_` was renamed to `estimator_`.

    `base_estimator_`estimator

      Estimator used to grow the ensemble.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    estimators_list of estimators

      The collection of fitted sub-estimators.

    `estimators_samples_`list of arrays

      The subset of drawn samples for each base estimator.

    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...])

  Methods

  `fit`(X, y[, sample_weight]) | Build a Bagging ensemble of estimators from the training set (X, y).
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict regression target for X.
  `score`(X, y[, sample_weight]) | Return the coefficient of determination of the prediction.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  property base_estimator_

    Estimator used to grow the ensemble.

  property estimators_samples_

    The subset of drawn samples for each base estimator.

    Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples.

    Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected.

  fit(X, y, sample_weight=None)

    Build a Bagging ensemble of estimators from the training set (X, y).

    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.

      y array-like of shape (n_samples,)

        The target values (class labels in classification, real numbers in regression).

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting.

    Returns:
      self object

        Fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(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.

  score(X, y, sample_weight=None)

    Return the coefficient of determination of the prediction.

    The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares `((y_true - y_pred)** 2).sum()` and \(v\) is the total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a \(R^2\) score of 0.0.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True values for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        \(R^2\) of `self.predict(X)` w.r.t. `y`.

    Notes

    The \(R^2\) score used when calling `score` on a regressor uses `multioutput='uniform_average'` from version 0.23 to keep consistent with default value of `r2_score`. This influences the `score` method of all the multioutput regressors (except for `MultiOutputRegressor`).

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BaggingRegressor

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → BaggingRegressor

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.ensemble.BaggingRegressor`

Single estimator versus bagging: bias-variance decomposition

Single estimator versus bagging: bias-variance decomposition