sklearn.ensemble.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.

Read more in the User Guide.

New in version 0.21.

Parameters
estimatorslist of (str, estimator) tuples

Invoking the fit method on the VotingRegressor will fit clones of those original estimators that will be stored in the class attribute self.estimators_. An estimator can be set to 'drop' using set_params.

Changed in version 0.21: 'drop' is accepted. Using None was deprecated in 0.22 and support was removed in 0.24.

weightsarray-like of shape (n_regressors,), default=None

Sequence of weights (float or int) to weight the occurrences of predicted values before averaging. Uses uniform weights if None.

n_jobsint, default=None

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

verbosebool, default=False

If True, the time elapsed while fitting will be printed as it is completed.

New in version 0.23.

Attributes
estimators_list of regressors

The collection of fitted sub-estimators as defined in estimators that are not ‘drop’.

named_estimators_Bunch

Attribute to access any fitted sub-estimators by name.

New in version 0.20.

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. .. versionadded:: 1.0

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

Methods

fit(X, y[, sample_weight])

Fit the estimators.

fit_transform(X[, y])

Return class labels or probabilities for each estimator.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_params([deep])

Get the parameters of an estimator from the ensemble.

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 an estimator from the ensemble.

transform(X)

Return predictions for X for each estimator.

fit(X, y, sample_weight=None)[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.

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

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_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 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)[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 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) wrt. 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_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.

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.

Examples using sklearn.ensemble.VotingRegressor