`sklearn.ensemble`.VotingRegressor¶

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.

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`(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.

Parameters

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.

Returns

selfobject

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.

Parameters

Xnumpy array of shape [n_samples, n_features]: Training set.
ynumpy array of shape [n_samples]: Target values.

Returns

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

Parameters

deepbool: 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.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): The input samples.

Returns

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.

Parameters

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.

Returns

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

Notes

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().

Parameters

**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.

Examples

# 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.

Parameters

X{array-like, sparse matrix}, shape (n_samples, n_features): The input samples.

Returns

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

sklearn.ensemble.VotingRegressor¶

`sklearn.ensemble`.VotingRegressor¶