sklearn.multioutput.MultiOutputClassifier

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

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.

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_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_params(**params)	Set the parameters of this estimator.

fit(X, Y, sample_weight=None, **fit_params)

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)

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)

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)

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)

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)

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)

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.