sklearn.ensemble
.BaggingRegressor¶
- class sklearn.ensemble.BaggingRegressor(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 regressor.
A Bagging regressor is an ensemble meta-estimator 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 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
DecisionTreeRegressor
.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.
- 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_
estimatorEstimator used to grow the ensemble.
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 sub-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.
- 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_prediction_ndarray of shape (n_samples,)
Prediction 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_prediction_
might contain NaN. This attribute exists only whenoob_score
is True.
See also
BaggingClassifier
A Bagging classifier.
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 SVR >>> from sklearn.ensemble import BaggingRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_samples=100, n_features=4, ... n_informative=2, n_targets=1, ... random_state=0, shuffle=False) >>> regr = BaggingRegressor(estimator=SVR(), ... n_estimators=10, random_state=0).fit(X, y) >>> regr.predict([[0, 0, 0, 0]]) array([-2.8720...])
Methods
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])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
- property base_estimator_¶
Estimator used to grow the ensemble.
- property estimator_¶
Estimator used to grow the ensemble.
- 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 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:
- 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 values.
- score(X, y, sample_weight=None)[source]¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 ofy
, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
\(R^2\) of
self.predict(X)
wrt.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- 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.
Examples using sklearn.ensemble.BaggingRegressor
¶
Single estimator versus bagging: bias-variance decomposition