`sklearn.ensemble`.BaggingRegressor¶

class sklearn.ensemble.BaggingRegressor(base_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)[source]¶

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 [R4d113ba76fc0-1]. If samples are drawn with replacement, then the method is known as Bagging [R4d113ba76fc0-2]. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [R4d113ba76fc0-3]. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [R4d113ba76fc0-4].

Read more in the User Guide.

New in version 0.15.

Parameters

base_estimatorobject or None, optional (default=None)

The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree.

n_estimatorsint, optional (default=10)

The number of base estimators in the ensemble.

max_samplesint or float, optional (default=1.0)

The number of samples to draw from X to train each base estimator.

If int, then draw max_samples samples.
If float, then draw max_samples * X.shape[0] samples.

max_featuresint or float, optional (default=1.0)

The number of features to draw from X to train each base estimator.

If int, then draw max_features features.
If float, then draw max_features * X.shape[1] features.

bootstrapboolean, optional (default=True)

Whether samples are drawn with replacement. If False, sampling without replacement is performed.

bootstrap_featuresboolean, optional (default=False)

Whether features are drawn with replacement.

oob_scorebool

Whether to use out-of-bag samples to estimate the generalization error.

warm_startbool, 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 ensemble. See the Glossary.

n_jobsint or None, optional (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_stateint, 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.

verboseint, optional (default=0)

Controls the verbosity when fitting and predicting.

Attributes

base_estimator_estimator: The base estimator from which the ensemble is grown.
n_features_int: The number of features when fit is performed.
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.

References

R4d113ba76fc0-1: L. Breiman, “Pasting small votes for classification in large databases and on-line”, Machine Learning, 36(1), 85-103, 1999.
R4d113ba76fc0-2: L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123-140, 1996.
R4d113ba76fc0-3: T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.
R4d113ba76fc0-4: 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(base_estimator=SVR(),
...                         n_estimators=10, random_state=0).fit(X, y)
>>> regr.predict([[0, 0, 0, 0]])
array([-2.8720...])

Methods

`fit`(self, X, y[, sample_weight])	Build a Bagging ensemble of estimators from the training
`get_params`(self[, deep])	Get parameters for this estimator.
`predict`(self, X)	Predict regression target for X.
`score`(self, X, y[, sample_weight])	Return the coefficient of determination R^2 of the prediction.
`set_params`(self, \\params)	Set the parameters of this estimator.

__init__(self, base_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)[source]¶: Initialize self. See help(type(self)) for accurate signature.

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(self, X, y, sample_weight=None)[source]¶

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.
yarray-like of shape (n_samples,): The target values (class labels in classification, real numbers in regression).
sample_weightarray-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

selfobject

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

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

Returns

paramsmapping of string to any: Parameter names mapped to their values.

predict(self, X)[source]¶

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

yndarray of shape (n_samples,): The predicted values.

score(self, X, y, sample_weight=None)[source]¶

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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

Xarray-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, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

scorefloat: R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

set_params(self, **params)[source]¶

Set the parameters of this estimator.

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

Parameters

**paramsdict: Estimator parameters.

Returns

selfobject: Estimator instance.

Examples using `sklearn.ensemble.BaggingRegressor`¶

Single estimator versus bagging: bias-variance decomposition¶

sklearn.ensemble.BaggingRegressor¶

Examples using sklearn.ensemble.BaggingRegressor¶

`sklearn.ensemble`.BaggingRegressor¶

Examples using `sklearn.ensemble.BaggingRegressor`¶