sklearn.ensemble
.BaggingClassifier¶

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.
New in version 0.15.
 Parameters
 base_estimatorobject, default=None
The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a
DecisionTreeClassifier
. n_estimatorsint, default=10
The number of base estimators in the ensemble.
 max_samplesint 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_featuresint 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_features * X.shape[1]
features.
 bootstrapbool, default=True
Whether samples are drawn with replacement. If False, sampling without replacement is performed.
 bootstrap_featuresbool, default=False
Whether features are drawn with replacement.
 oob_scorebool, default=False
Whether to use outofbag samples to estimate the generalization error. Only available if bootstrap=True.
 warm_startbool, 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_jobsint, default=None
The number of jobs to run in parallel for both
fit
andpredict
.None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. random_stateint, 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. verboseint, 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 base estimators.
estimators_samples_
list of arraysThe 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 outofbag estimate. This attribute exists only when
oob_score
is True. oob_decision_function_ndarray of shape (n_samples, n_classes)
Decision function computed with outofbag 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 whenoob_score
is True.
References
 1
L. Breiman, “Pasting small votes for classification in large databases and online”, Machine Learning, 36(1), 85103, 1999.
 2
L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123140, 1996.
 3
T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832844, 1998.
 4
G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346361, 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(base_estimator=SVC(), ... n_estimators=10, random_state=0).fit(X, y) >>> clf.predict([[0, 0, 0, 0]]) array([1])
Methods
Average of the decision functions of the base classifiers.
fit
(X, y[, sample_weight])Build a Bagging ensemble of estimators from the training
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict class for X.
Predict class logprobabilities for X.
Predict class probabilities for X.
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.

decision_function
(X)[source]¶ Average of the decision functions of the base classifiers.
 Parameters
 X{arraylike, 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
 scorendarray 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 withk == 1
, otherwisek==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 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]¶  Build a Bagging ensemble of estimators from the training
set (X, y).
 Parameters
 X{arraylike, 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.
 yarraylike of shape (n_samples,)
The target values (class labels in classification, real numbers in regression).
 sample_weightarraylike 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
(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
 paramsdict
Parameter names mapped to their values.

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
 X{arraylike, 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 classes.

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
 X{arraylike, 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
 pndarray of shape (n_samples, n_classes)
The class logprobabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

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
 X{arraylike, 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
 pndarray 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)[source]¶ Return 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of
self.predict(X)
wrt.y
.

set_params
(**params)[source]¶ 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
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.