sklearn.ensemble.StackingRegressor

class sklearn.ensemble.StackingRegressor(estimators, final_estimator=None, *, cv=None, n_jobs=None, passthrough=False, verbose=0)

Stack of estimators with a final regressor.

Stacked generalization consists in stacking the output of individual estimator and use a regressor to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator.

Note that estimators_ are fitted on the full X while final_estimator_ is trained using cross-validated predictions of the base estimators using cross_val_predict.

Read more in the User Guide.

New in version 0.22.

Parameters:

estimatorslist of (str, estimator)

Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set_params.

final_estimatorestimator, default=None

A regressor which will be used to combine the base estimators. The default regressor is a RidgeCV.

cvint, cross-validation generator, iterable, or “prefit”, default=None

Determines the cross-validation splitting strategy used in cross_val_predict to train final_estimator. Possible inputs for cv are:

• None, to use the default 5-fold cross validation,

• integer, to specify the number of folds in a (Stratified) KFold,

• An object to be used as a cross-validation generator,

• An iterable yielding train, test splits.

• “prefit” to assume the estimators are prefit, and skip cross validation

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

Refer User Guide for the various cross-validation strategies that can be used here.

If “prefit” is passed, it is assumed that all estimators have been fitted already. The final_estimator_ is trained on the estimators predictions on the full training set and are not cross validated predictions. Please note that if the models have been trained on the same data to train the stacking model, there is a very high risk of overfitting.

New in version 1.1: The ‘prefit’ option was added in 1.1

Note

A larger number of split will provide no benefits if the number of training samples is large enough. Indeed, the training time will increase. cv is not used for model evaluation but for prediction.

n_jobsint, default=None

The number of jobs to run in parallel for fit of all estimators. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

passthroughbool, default=False

When False, only the predictions of estimators will be used as training data for final_estimator. When True, the final_estimator is trained on the predictions as well as the original training data.

verboseint, default=0

Verbosity level.

Attributes:

estimators_list of estimator

The elements of the estimators parameter, having been fitted on the training data. If an estimator has been set to 'drop', it will not appear in estimators_. When cv="prefit", estimators_ is set to estimators and is not fitted again.

named_estimators_Bunch

Attribute to access any fitted sub-estimators by name.

n_features_in_int

Number of features seen during fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Only defined if the underlying estimators expose such an attribute when fit.

New in version 1.0.

final_estimator_estimator

The regressor to stacked the base estimators fitted.

stack_method_list of str

The method used by each base estimator.

See also

StackingClassifier

Stack of estimators with a final classifier.

References

[1]

Wolpert, David H. “Stacked generalization.” Neural networks 5.2 (1992): 241-259.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import RidgeCV
>>> from sklearn.svm import LinearSVR
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.ensemble import StackingRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> estimators = [
...     ('lr', RidgeCV()),
...     ('svr', LinearSVR(dual="auto", random_state=42))
... ]
>>> reg = StackingRegressor(
...     estimators=estimators,
...     final_estimator=RandomForestRegressor(n_estimators=10,
...                                           random_state=42)
... )
>>> from sklearn.model_selection import train_test_split
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=42
... )
>>> reg.fit(X_train, y_train).score(X_test, y_test)
0.3...

Methods

fit(X, y[, sample_weight])	Fit the estimators.
fit_transform(X, y[, sample_weight])	Fit the estimators and return the predictions for X for each estimator.
get_feature_names_out([input_features])	Get output feature names for transformation.
get_metadata_routing()	Raise NotImplementedError.
get_params([deep])	Get the parameters of an estimator from the ensemble.
predict(X, **predict_params)	Predict target for X.
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_fit_request(*[, sample_weight])	Request metadata passed to the fit method.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of an estimator from the ensemble.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.
transform(X)	Return the predictions for X for each estimator.

fit(X, y, sample_weight=None)

Fit the estimators.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target values.

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 all underlying estimators support sample weights.

Returns:

selfobject

Returns a fitted instance.

fit_transform(X, y, sample_weight=None)

Fit the estimators and return the predictions for X for each estimator.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target values.

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 all underlying estimators support sample weights.

Returns:

y_predsndarray of shape (n_samples, n_estimators)

Prediction outputs for each estimator.

get_feature_names_out(input_features=None)

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features. The input feature names are only used when passthrough is True.

• If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then names are generated: [x0, x1, ..., x(n_features_in_ - 1)].

• If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

If passthrough is False, then only the names of estimators are used to generate the output feature names.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()

Raise NotImplementedError.

This estimator does not support metadata routing yet.

get_params(deep=True)

Get the parameters of an estimator from the ensemble.

Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter.

Parameters:

deepbool, default=True

Setting it to True gets the various estimators and the parameters of the estimators as well.

Returns:

paramsdict

Parameter and estimator names mapped to their values or parameter names mapped to their values.

property n_features_in_

Number of features seen during fit.

property named_estimators

Dictionary to access any fitted sub-estimators by name.

Returns:

Bunch

predict(X, **predict_params)

Predict target for X.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

**predict_paramsdict of str -> obj

Parameters to the predict called by the final_estimator. Note that this may be used to return uncertainties from some estimators with return_std or return_cov. Be aware that it will only accounts for uncertainty in the final estimator.

Returns:

y_predndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.

score(X, y, sample_weight=None)

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 of y, 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), where n_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) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StackingRegressor

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

New in version 1.4: "polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

Set the parameters of an estimator from the ensemble.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators.

Parameters:

**paramskeyword arguments

Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.

Returns:

selfobject

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StackingRegressor

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.

transform(X)

Return the predictions for X for each estimator.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns:

y_predsndarray of shape (n_samples, n_estimators)

Prediction outputs for each estimator.

Examples using sklearn.ensemble.StackingRegressor

Combine predictors using stacking

Combine predictors using stacking