`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')[source]¶

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].

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_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_params`(**params)	Set the parameters of this estimator.

property base_estimator_¶: Estimator used to grow the ensemble.

decision_function(X)[source]¶

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:

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 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)[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: Fitted estimator.

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{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 classes.

predict_log_proba(X)[source]¶

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:

pndarray 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)[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{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:

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 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:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. 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.

sklearn.ensemble.BaggingClassifier¶

`sklearn.ensemble`.BaggingClassifier¶