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# `sklearn.ensemble`.BaggingClassifier

class sklearn.ensemble.BaggingClassifier(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 classifier.

  A Bagging classifier is an ensemble meta-estimator that fits base classifiers 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 `DecisionTreeClassifier`.

      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.

      New in version 0.17: warm_start constructor parameter.

    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 base 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.

    classes_ndarray of shape (n_classes,)

      The classes labels.

    n_classes_int or list

      The number of classes.

    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_decision_function_ndarray of shape (n_samples, n_classes)

      Decision function 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_decision_function_` might contain NaN. This attribute exists only when `oob_score` is True.

  See also

  `BaggingRegressor`

    A Bagging regressor.

  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 SVC
      >>> from sklearn.ensemble import BaggingClassifier
      >>> from sklearn.datasets import make_classification
      >>> X, y = make_classification(n_samples=100, n_features=4,
      ...                            n_informative=2, n_redundant=0,
      ...                            random_state=0, shuffle=False)
      >>> clf = BaggingClassifier(estimator=SVC(),
      ...                         n_estimators=10, random_state=0).fit(X, y)
      >>> clf.predict([[0, 0, 0, 0]])
      array([1])

  Methods

  `decision_function`(X) | Average of the decision functions of the base classifiers.
  `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 class for X.
  `predict_log_proba`(X) | Predict class log-probabilities for X.
  `predict_proba`(X) | Predict class probabilities for X.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `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.

  decision_function(X)

    Average of the decision functions of the base classifiers.

    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:
      score ndarray of shape (n_samples, k)

        The decision function of the input samples. The columns correspond to the classes in sorted order, as they appear in the attribute `classes_`. Regression and binary classification are special cases with `k == 1`, otherwise `k==n_classes`.

  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 class for X.

    The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a `predict_proba` method, then it resorts to voting.

    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 classes.

  predict_log_proba(X)

    Predict class log-probabilities for X.

    The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the base 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:
      p ndarray of shape (n_samples, n_classes)

        The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

  predict_proba(X)

    Predict class probabilities for X.

    The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a `predict_proba` method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class.

    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:
      p ndarray of shape (n_samples, n_classes)

        The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

  score(X, y, sample_weight=None)

    Return the mean accuracy on the given test data and labels.

    In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

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

        Test samples.

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

        True labels for `X`.

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

        Sample weights.

    Returns:
      score float

        Mean accuracy of `self.predict(X)` w.r.t. `y`.

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

    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 = '$UNCHANGED$') → BaggingClassifier

    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.