MultiOutputRegressor#
- class sklearn.multioutput.MultiOutputRegressor(estimator, *, n_jobs=None)[source]#
Multi target regression.
This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.
Added in version 0.18.
- Parameters:
- estimatorestimator object
- n_jobsint or None, optional (default=None)
The number of jobs to run in parallel.
fit
,predict
andpartial_fit
(if supported by the passed estimator) will be parallelized for each target.When individual estimators are fast to train or predict, using
n_jobs > 1
can result in slower performance due to the parallelism overhead.None
means1
unless in ajoblib.parallel_backend
context.-1
means using all available processes / threads. See Glossary for more details.Changed in version 0.20:
n_jobs
default changed from1
toNone
.
- Attributes:
- estimators_list of
n_output
estimators Estimators used for predictions.
- n_features_in_int
Number of features seen during fit. Only defined if the underlying
estimator
exposes such an attribute when fit.Added in version 0.24.
- 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.
- estimators_list of
See also
RegressorChain
A multi-label model that arranges regressions into a chain.
MultiOutputClassifier
Classifies each output independently rather than chaining.
Examples
>>> import numpy as np >>> from sklearn.datasets import load_linnerud >>> from sklearn.multioutput import MultiOutputRegressor >>> from sklearn.linear_model import Ridge >>> X, y = load_linnerud(return_X_y=True) >>> regr = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y) >>> regr.predict(X[[0]]) array([[176, 35.1, 57.1]])
- fit(X, y, sample_weight=None, **fit_params)[source]#
Fit the model to data, separately for each output variable.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
- y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel estimation.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If
None
, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.- **fit_paramsdict of string -> object
Parameters passed to the
estimator.fit
method of each step.Added in version 0.23.
- Returns:
- selfobject
Returns a fitted instance.
- get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.3.
- Returns:
- routingMetadataRouter
A
MetadataRouter
encapsulating routing information.
- get_params(deep=True)[source]#
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- partial_fit(X, y, sample_weight=None, **partial_fit_params)[source]#
Incrementally fit the model to data, for each output variable.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
- y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If
None
, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.- **partial_fit_paramsdict of str -> object
Parameters passed to the
estimator.partial_fit
method of each sub-estimator.Only available if
enable_metadata_routing=True
. See the User Guide.Added in version 1.3.
- Returns:
- selfobject
Returns a fitted instance.
- predict(X)[source]#
Predict multi-output variable using model for each target variable.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
- Returns:
- y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
- score(X, y, sample_weight=None)[source]#
Return coefficient of determination on test data.
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 = '$UNCHANGED$') MultiOutputRegressor [source]#
Configure whether metadata should be requested to be passed to the
fit
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config
). Please check the 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.Added in version 1.3.
- 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_params(**params)[source]#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') MultiOutputRegressor [source]#
Configure whether metadata should be requested to be passed to the
partial_fit
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_fit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topartial_fit
.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.Added in version 1.3.
- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inpartial_fit
.
- Returns:
- selfobject
The updated object.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') MultiOutputRegressor [source]#
Configure whether metadata should be requested to be passed to the
score
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config
). Please check the 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.Added in version 1.3.
- 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.
Gallery examples#

Comparing random forests and the multi-output meta estimator