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].
Read more in the User Guide.
New in version 0.15.
- Parameters:
- estimatorobject, default=None
The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a
DecisionTreeClassifier
.New in version 1.2:
base_estimator
was renamed toestimator
.- 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(1, int(max_features * n_features_in_))
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 out-of-bag 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.
- base_estimatorobject, default=”deprecated”
Use
estimator
instead.Deprecated since version 1.2:
base_estimator
is deprecated and will be removed in 1.4. Useestimator
instead.
- Attributes:
- estimator_estimator
The base estimator from which the ensemble is grown.
New in version 1.2:
base_estimator_
was renamed toestimator_
.base_estimator_
estimatorEstimator used to grow the ensemble.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
- 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 out-of-bag estimate. This attribute exists only when
oob_score
is True.- oob_decision_function_ndarray of shape (n_samples, n_classes)
Decision function 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_decision_function_
might contain NaN. This attribute exists only whenoob_score
is True.
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
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 class log-probabilities 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.
- 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 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 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.