MultiOutputClassifier#

classsklearn.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 implementingfit andpredict.Apredict_proba method will be exposed only ifestimator implementsit.

n_jobsint or None, optional (default=None)

The number of jobs to run in parallel.fit,predict andpartial_fit (if supportedby the passed estimator) will be parallelized for each target.

When individual estimators are fast to train or predict,usingn_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.SeeGlossary 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 ofn_output estimators

Estimators used for predictions.

n_features_in_int

Number of features seen duringfit. Only defined if theunderlyingestimator exposes such an attribute when fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Only defined if theunderlying estimators expose such an attribute when fit.

Added 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

>>>importnumpyasnp>>>fromsklearn.datasetsimportmake_multilabel_classification>>>fromsklearn.multioutputimportMultiOutputClassifier>>>fromsklearn.linear_modelimportLogisticRegression>>>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]])
fit(X,Y,sample_weight=None,**fit_params)[source]#

Fit the model to data matrix X and targets Y.

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. IfNone, then samples are equally weighted.Only supported if the underlying classifier supports sampleweights.

**fit_paramsdict of string -> object

Parameters passed to theestimator.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 checkUser Guide on how the routingmechanism works.

Added in version 1.3.

Returns:
routingMetadataRouter

AMetadataRouter 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])foriinrange(y.shape[1])], whereyis 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. IfNone, then samples are equally weighted.Only supported if the underlying regressor supports sampleweights.

**partial_fit_paramsdict of str -> object

Parameters passed to theestimator.partial_fit method of eachsub-estimator.

Only available ifenable_metadata_routing=True. See theUser 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.

predict_proba(X)[source]#

Return prediction probabilities for each class of each output.

This method will raise aValueError 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 attributeclasses_.

Changed in version 0.19:This function now returns a list of arrays where the length ofthe list isn_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]#

Configure whether metadata should be requested to be passed to thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser 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.

Added in version 1.3.

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

Metadata routing forsample_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 asPipeline). 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]#

Configure whether metadata should be requested to be passed to thepartial_fit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser 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.

Added in version 1.3.

Parameters:
classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forclasses parameter inpartial_fit.

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

Metadata routing forsample_weight parameter inpartial_fit.

Returns:
selfobject

The updated object.