package sklearn

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 :ref:`User Guide <bagging>`.

.. versionadded:: 0.15

Parameters ---------- base_estimator : object, default=None The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree.

n_estimators : int, default=10 The number of base estimators in the ensemble.

max_samples : int 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.shape0` samples.

max_features : int 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.shape1` features.

bootstrap : bool, default=True Whether samples are drawn with replacement. If False, sampling without replacement is performed.

bootstrap_features : bool, default=False Whether features are drawn with replacement.

oob_score : bool, default=False Whether to use out-of-bag samples to estimate the generalization error.

warm_start : bool, 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 :term:`the Glossary <warm_start>`.

n_jobs : int, default=None The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

random_state : int or RandomState, 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 :term:`Glossary <random_state>`.

verbose : int, 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 :meth:`fit` is performed.

estimators_ : list of estimators The collection of fitted sub-estimators.

estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by an array of the indices selected.

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 when ``oob_score`` is True.

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(base_estimator=SVR(), ... n_estimators=10, random_state=0).fit(X, y) >>> regr.predict([0, 0, 0, 0]) array(-2.8720...)

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.

Return the index'th estimator in the ensemble.

Return iterator over estimators in the ensemble.

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.

y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression).

sample_weight : array-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 ------- self : object

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

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 ------- y : ndarray of shape (n_samples,) The predicted values.

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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 of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters ---------- X : array-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, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float R^2 of self.predict(X) wrt. y.

Notes ----- The R2 score used when calling ``score`` on a regressor uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).

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.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Attribute base_estimator_: get value or raise Not_found if None.

Attribute base_estimator_: get value as an option.

Attribute n_features_: get value or raise Not_found if None.

Attribute n_features_: get value as an option.

Attribute estimators_: get value or raise Not_found if None.

Attribute estimators_: get value as an option.

Attribute estimators_samples_: get value or raise Not_found if None.

Attribute estimators_samples_: get value as an option.

Attribute estimators_features_: get value or raise Not_found if None.

Attribute estimators_features_: get value as an option.

Attribute oob_score_: get value or raise Not_found if None.

Attribute oob_score_: get value as an option.

Attribute oob_prediction_: get value or raise Not_found if None.

Attribute oob_prediction_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.