VotingRegressor (original) (raw)

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.

For a detailed example, refer toPlot individual and voting regression predictions.

Read more in the User Guide.

Added 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 attributeself.estimators_. An estimator can be set to 'drop' usingset_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 Glossaryfor more details.

verbosebool, default=False

If True, the time elapsed while fitting will be printed as it is completed.

Added in version 0.23.

Attributes:

**estimators_**list of regressors

The collection of fitted sub-estimators as defined in estimatorsthat are not ‘drop’.

named_estimators_Bunch

Attribute to access any fitted sub-estimators by name.

Added 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.

Added in version 1.0.

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 withset_params and fit the remaining two estimators:

er = er.set_params(lr='drop') er = er.fit(X, y) len(er.estimators_) 2

fit(X, y, *, sample_weight=None, **fit_params)[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 andn_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.

**fit_paramsdict

Parameters to pass to the underlying estimators.

Added in version 1.5: Only available if enable_metadata_routing=True, which can be set by usingsklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.

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_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_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.5.

Returns:

routingMetadataRouter

A MetadataRouter encapsulating routing information.

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_fittedis 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) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor usesmultioutput='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 forMultiOutputRegressor).

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VotingRegressor[source]#

Request metadata passed to the fit method.

Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output APIfor an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

Added in version 1.4: "polars" option was added.

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 inestimators.

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VotingRegressor[source]#

Request metadata passed to the score method.

Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.

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.