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.       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
>>> 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(er.fit(X, y).predict(X))
[ 3.3  5.7 11.8 19.7 28.  40.3]
 Methods  
fit(X, y[, sample_weight]) Fit the estimators.  
fit_transform(X[, y]) Return class labels or probabilities for each estimator.  
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 \(R^2\) 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_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.     
  
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 \(R^2\) of the prediction. The coefficient \(R^2\) 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’.     
  
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 
 predictions: ndarray of shape (n_samples, n_classifiers)

Values predicted by each regressor.