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..., 57...]])
- 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 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 = '$UNCHANgED$') MultiOutputRegressor [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.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 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_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]#
Request metadata passed to the
partial_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 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.
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 inpartial_fit
.
- Returns:
- selfobject
The updated object.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANgED$') MultiOutputRegressor [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.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 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.
gallery examples#
Comparing random forests and the multi-output meta estimator