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 metaestimator 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 metaestimator can typically be used as a way to reduce the variance of a blackbox 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.
Parameters: 


Attributes: 

[1]  (1, 2) L. Breiman, “Pasting small votes for classification in large databases and online”, Machine Learning, 36(1), 85103, 1999. 
[2]  (1, 2) L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123140, 1996. 
[3]  (1, 2) T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832844, 1998. 
[4]  (1, 2) G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346361, 2012. 
fit (X, y[, sample_weight])  Build a Bagging ensemble of estimators from the training set (X, y). 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict regression target for X. 
score (X, y[, sample_weight])  Returns the coefficient of determination R^2 of the prediction. 
set_params (**params)  Set the parameters of this estimator. 
__init__(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]
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 inbag samples.
Note: the list is recreated 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)
[source]
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

predict(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: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns 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: 


Returns: 

set_params(**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.
Returns: 


sklearn.ensemble.BaggingRegressor
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html