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.

New in version 1.0.

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 regression target for X. score(X, y[, sample_weight]) Return the coefficient of determination of the prediction. set_output(*[, transform]) Set output container. set_params(**params) Set the parameters of an estimator from the ensemble. 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

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_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”}, default=None

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• None: Transform configuration is unchanged

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.

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¶

Plot individual and voting regression predictions

Plot individual and voting regression predictions