# sklearn.multioutput.MultiOutputClassifier¶

class sklearn.multioutput.MultiOutputClassifier(estimator, *, n_jobs=None)[source]

Multi target classification.

This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification.

Parameters:
estimatorestimator object

An estimator object implementing fit and predict. A predict_proba method will be exposed only if estimator implements it.

n_jobsint or None, optional (default=None)

The number of jobs to run in parallel. fit, predict and partial_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 means 1 unless in a joblib.parallel_backend context. -1 means using all available processes / threads. See Glossary for more details.

Changed in version 0.20: n_jobs default changed from 1 to None.

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 the underlying 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 the underlying estimators expose such an attribute when fit.

New in version 1.0.

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_params([deep]) Get parameters for this estimator. partial_fit(X, y[, classes, sample_weight]) Incrementally fit a separate model for each class output. Predict multi-output variable using model for each target variable. Return prediction probabilities for each class of each output. score(X, y) Return the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of this estimator.
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. If None, then samples are equally weighted. Only supported if the underlying classifier supports sample weights.

**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_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, classes=None, sample_weight=None)[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])], where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.

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.

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 the estimators do not have predict_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 the classes corresponds to that in the attribute classes_.

Changed in version 0.19: This function now returns a list of arrays where the length of the 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_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.