`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.

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 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.

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`¶

sklearn.ensemble.VotingRegressor¶

Examples using sklearn.ensemble.VotingRegressor¶

`sklearn.ensemble`.VotingRegressor¶

Examples using `sklearn.ensemble.VotingRegressor`¶