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

Prediction voting regressor for unfitted estimators.

New in version 0.21.

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

Read more in the User Guide.

estimatorslist of (string, 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 None or 'drop' using set_params.

weightsarray-like, shape (n_regressors,), optional (default=`None`)

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

n_jobsint or None, optional (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.

estimators_list of regressors

The collection of fitted sub-estimators as defined in estimators that are not None.

named_estimators_Bunch object, a dictionary with attribute access

Attribute to access any fitted sub-estimators by name.

See also


Soft Voting/Majority Rule classifier.


>>> import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.ensemble import VotingRegressor
>>> r1 = LinearRegression()
>>> r2 = RandomForestRegressor(n_estimators=10, random_state=1)
>>> 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)])
>>> print(, y).predict(X))
[ 3.3  5.7 11.8 19.7 28.  40.3]


fit(self, X, y[, sample_weight])

Fit the estimators.

fit_transform(self, X[, y])

Fit to data, then transform it.

get_params(self[, deep])

Get the parameters of the ensemble estimator

predict(self, X)

Predict regression target for X.

score(self, X, y[, sample_weight])

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

set_params(self, \*\*params)

Setting the parameters for the ensemble estimator

transform(self, X)

Return predictions for X for each estimator.

__init__(self, estimators, weights=None, n_jobs=None)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y, sample_weight=None)[source]

Fit the estimators.

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

Target values.

sample_weightarray-like, shape (n_samples,) or None

Sample weights. If None, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights.

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

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)[source]

Get the parameters of the ensemble estimator


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

predict(self, 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.

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

The input samples.

yarray of shape (n_samples,)

The predicted values.

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

Returns 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.

Xarray-like, shape = (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like, shape = (n_samples) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like, shape = [n_samples], optional

Sample weights.


R^2 of self.predict(X) wrt. y.


The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

set_params(self, **params)[source]

Setting the parameters for the ensemble estimator

Valid parameter keys can be listed with get_params().

**paramskeyword arguments

Specific parameters using e.g. set_params(parameter_name=new_value) In addition, to setting the parameters of the ensemble estimator, the individual estimators of the ensemble estimator can also be set or replaced by setting them to None.


# In this example, the RandomForestClassifier is removed clf1 = LogisticRegression() clf2 = RandomForestClassifier() eclf = VotingClassifier(estimators=[(‘lr’, clf1), (‘rf’, clf2)] eclf.set_params(rf=None)

transform(self, X)[source]

Return predictions for X for each estimator.

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

The input samples.


array-like of shape (n_samples, n_classifiers), being values predicted by each regressor.

Examples using sklearn.ensemble.VotingRegressor