- class sklearn.multioutput.MultiOutputClassifier(estimator, *, n_jobs=None)[source]¶
Multi target classification.
This strategy consists of fitting one classifier per target. This is asimple strategy for extending classifiers that do not natively supportmulti-target classification.
- Parameters:
- estimatorestimator object
An estimator object implementing fit and predict.A predict_proba method will be exposed only if
estimator
implementsit.- n_jobsint or None, optional (default=None)
The number of jobs to run in parallel.fit, predict and partial_fit (if supportedby 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 dueto 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:
- classes_ndarray of shape (n_classes,)
Class labels.
- estimators_list of
n_output
estimators Estimators used for predictions.
- n_features_in_int
Number of features seen during fit. Only defined if theunderlying
estimator
exposes such an attribute when fit.New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Only defined if theunderlying estimators expose such an attribute when fit.
New in version 1.0.
See also
- ClassifierChain
A multi-label model that arranges binary classifiers into a chain.
- MultiOutputRegressor
Fits one regressor per target variable.
Examples
>>> import numpy as np>>> from sklearn.datasets import make_multilabel_classification>>> from sklearn.multioutput import MultiOutputClassifier>>> from sklearn.linear_model import LogisticRegression>>> X, y = make_multilabel_classification(n_classes=3, random_state=0)>>> clf = MultiOutputClassifier(LogisticRegression()).fit(X, y)>>> clf.predict(X[-2:])array([[1, 1, 1], [1, 0, 1]])
Methods
fit(X,Y[,sample_weight])
Fit the model to data matrix X and targets Y.
get_metadata_routing()
Get metadata routing of this object.
get_params([deep])
Get parameters for this estimator.
partial_fit(X,y[,classes,sample_weight])
Incrementally fit a separate model for each class output.
predict(X)
Predict multi-output variable using model for each target variable.
predict_proba(X)
Return prediction probabilities for each class of each output.
score(X,y)
Return the mean accuracy on the given test data and labels.
set_fit_request(*[,sample_weight])
Request metadata passed to the
fit
method.set_params(**params)
Set the parameters of this estimator.
set_partial_fit_request(*[,classes,...])
Request metadata passed to the
partial_fit
method.- fit(X, Y, sample_weight=None, **fit_params)[source]¶
Fit the model to data matrix X and targets Y.
See Also1.12. Multiclass and multioutput algorithms - scikit-learn DocumentationBladesinger and hexblade combo - Wizard - Class Forums - D&D Beyond ForumsBladesinger 5e | A Wizard's Guide to Martial Spellcasting1.12. Multiclass and multioutput algorithms- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
- Yarray-like of shape (n_samples, n_classes)
The target values.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If
None
, then samples are equally weighted.Only supported if the underlying classifier supports sampleweights.- **fit_paramsdict of string -> object
Parameters passed to the
estimator.fit
method of each step.New 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 routingmechanism works.
New in version 1.3.
- Returns:
- routingMetadataRouter
A MetadataRouter encapsulatingrouting information.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator andcontained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- partial_fit(X, y, classes=None, sample_weight=None, **partial_fit_params)[source]¶
Incrementally fit a separate model for each class output.
- 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.
- classeslist of ndarray of shape (n_outputs,), default=None
Each array is unique classes for one output in str/int.Can be obtained via
[np.unique(y[:, i]) for i in range(y.shape[1])]
, wherey
is the target matrix of the entire dataset.This argument is required for the first call to partial_fitand can be omitted in the subsequent calls.Note thaty
doesn’t need to contain all labels inclasses
.- 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 sampleweights.- **partial_fit_paramsdict of str -> object
Parameters passed to the
estimator.partial_fit
method of eachsub-estimator.Only available if
enable_metadata_routing=True
. See theUser Guide.New 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.
- predict_proba(X)[source]¶
Return prediction probabilities for each class of each output.
This method will raise a
ValueError
if any of theestimators do not havepredict_proba
.- Parameters:
- Xarray-like of shape (n_samples, n_features)
The input data.
- Returns:
- parray of shape (n_samples, n_classes), or a list of n_outputs such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of theclasses corresponds to that in the attribute classes_.
Changed in version 0.19: This function now returns a list of arrays where the length ofthe list is
n_outputs
, and each array is (n_samples
,n_classes
) for that particular output.
- score(X, y)[source]¶
Return the mean accuracy on the given test data and labels.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples, n_outputs)
True values for X.
- Returns:
- scoresfloat
Mean accuracy of predicted target versus true target.
- set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MultiOutputClassifier[source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(see sklearn.set_config).Please see User Guide on how the routingmechanism 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 theexisting request. This allows you to change the request for someparameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as asub-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 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 haveparameters of the form
<component>__<parameter>
so that it’spossible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → MultiOutputClassifier[source]¶
Request metadata passed to the
partial_fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(see sklearn.set_config).Please see User Guide on how the routingmechanism 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 theexisting request. This allows you to change the request for someparameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as asub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.
- Parameters:
- classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
classes
parameter inpartial_fit
.- 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.
sklearn.multioutput.MultiOutputClassifier (2024)
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