VotingRegressor#

class sklearn.ensemble.VotingRegressor(estimators, *, weights=None, n_jobs=None, verbose=False)[source]#

Prediction voting regressor for unfitted estimators.

A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction.

For a detailed example, refer to Plot individual and voting regression predictions.

See also

VotingClassifier: Soft Voting/Majority Rule classifier.

Examples

>>> import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.ensemble import VotingRegressor
>>> from sklearn.neighbors import KNeighborsRegressor
>>> r1 = LinearRegression()
>>> r2 = RandomForestRegressor(n_estimators=10, random_state=1)
>>> r3 = KNeighborsRegressor()
>>> X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]])
>>> y = np.array([2, 6, 12, 20, 30, 42])
>>> er = VotingRegressor([('lr', r1), ('rf', r2), ('r3', r3)])
>>> print(er.fit(X, y).predict(X))
[ 6.8  8.4 12.5 17.8 26  34]

In the following example, we drop the 'lr' estimator with set_params and fit the remaining two estimators:

>>> er = er.set_params(lr='drop')
>>> er = er.fit(X, y)
>>> len(er.estimators_)
2

fit(X, y, **fit_params)[source]#

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.
**fit_paramsdict: Parameters to pass to the underlying estimators.

Added in version 1.5: Only available if enable_metadata_routing=True, which can be set by using sklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.

Returns:

selfobject: Fitted estimator.

fit_transform(X, y=None, **fit_params)[source]#

Return class labels or probabilities for each estimator.

Return predictions for X for each estimator.

Parameters:

X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features): Input samples.
yndarray of shape (n_samples,), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None: Not used, present here for API consistency by convention.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.5.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)[source]#

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 named_estimators#

Dictionary to access any fitted sub-estimators by name.

Returns:

Bunch

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 input samples.

Returns:

yndarray of shape (n_samples,): The predicted values.

score(X, y, sample_weight=None)[source]#

Return coefficient of determination on test data.

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_output(*, transform=None)[source]#

Set output container.

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

Parameters:

transform{“default”, “pandas”, “polars”}, 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

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

Returns:

selfestimator instance: Estimator instance.

set_params(**params)[source]#

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 = '$UNCHANGED$') → VotingRegressor[source]#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the 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.

Added in version 1.3.

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)[source]#

Return predictions for X for each estimator.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The input samples.

Returns:

predictionsndarray of shape (n_samples, n_classifiers): Values predicted by each regressor.

Gallery examples#

Plot individual and voting regression predictions

VotingRegressor#

Gallery examples#

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