class sklearn.ensemble.BaggingClassifier(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 classifier.
A Bagging classifier is an ensemble metaestimator 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 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. 
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_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict class for X. 
predict_log_proba (X)  Predict class logprobabilities for X. 
predict_proba (X)  Predict class probabilities for X. 
score (X, y[, sample_weight])  Returns the mean accuracy on the given test data and labels. 
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]
decision_function(X)
[source]
Average of the decision functions of the base classifiers.
Parameters: 


Returns: 

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 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: 


Returns: 

predict_log_proba(X)
[source]
Predict class logprobabilities for X.
The predicted class logprobabilities of an input sample is computed as the log of the mean predicted class probabilities of the base estimators in the ensemble.
Parameters: 


Returns: 

predict_proba(X)
[source]
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: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel 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: 


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: 


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