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 theVotingRegressor
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
orint
) to weight the occurrences of predicted values before averaging. Uses uniform weights ifNone
.- n_jobsint, default=None
The number of jobs to run in parallel for
fit
.None
means 1 unless in ajoblib.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_
intNumber 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.
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 >>> 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
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.
Raise
NotImplementedError
.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_fit_request
(*[, sample_weight])Request metadata passed to the
fit
method.set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of an estimator from the ensemble.
set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.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 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.
- 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]¶
Raise
NotImplementedError
.This estimator does not support metadata routing yet.
- 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.
- 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 ofy
, 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)
, wheren_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)
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 ofr2_score
. This influences thescore
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 if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New 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 a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns:
- selfobject
The updated object.
- 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
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame output"polars"
: Polars outputNone
: Transform configuration is unchanged
New 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 if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New 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 a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.
Examples using sklearn.ensemble.VotingRegressor
¶
Plot individual and voting regression predictions