# 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, score and predict_proba.

n_jobsint or None, optional (default=None)

The number of jobs to use for the computation. It does each target variable in y in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Attributes
estimators_list of n_output estimators

Estimators used for predictions.

Examples

>>> import numpy as np
>>> from sklearn.datasets import make_multilabel_classification
>>> from sklearn.multioutput import MultiOutputClassifier
>>> from sklearn.neighbors import KNeighborsClassifier

>>> X, y = make_multilabel_classification(n_classes=3, random_state=0)
>>> clf = MultiOutputClassifier(KNeighborsClassifier()).fit(X, y)
>>> clf.predict(X[-2:])
array([[1, 1, 0], [1, 1, 1]])


Methods

 fit(self, X, Y[, sample_weight]) Fit the model to data matrix X and targets Y. get_params(self[, deep]) Get parameters for this estimator. partial_fit(self, X, y[, classes, sample_weight]) Incrementally fit the model to data. predict(self, X) Predict multi-output variable using a model score(self, X, y) Returns the mean accuracy on the given test data and labels. set_params(self, \*\*params) Set the parameters of this estimator.
__init__(self, estimator, n_jobs=None)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, Y, sample_weight=None)[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,) or None

Sample weights. If None, then samples are equally weighted. Only supported if the underlying classifier supports sample weights.

Returns
selfobject
get_params(self, 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
paramsmapping of string to any

Parameter names mapped to their values.

partial_fit(self, X, y, classes=None, sample_weight=None)[source]

Incrementally fit the model to data. Fit a separate model for each output variable.

Parameters
X(sparse) array-like, shape (n_samples, n_features)

Data.

y(sparse) array-like, shape (n_samples, n_outputs)

Multi-output targets.

classeslist of numpy arrays, shape (n_outputs)

Each array is unique classes for one output in str/int Can be obtained by 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
predict(self, X)[source]
Predict multi-output variable using a model

trained for each target variable.

Parameters
X(sparse) array-like, shape (n_samples, n_features)

Data.

Returns
y(sparse) array-like, shape (n_samples, n_outputs)

Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.

property predict_proba

Probability estimates. Returns 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, shape (n_samples, n_features)

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_.

score(self, X, y)[source]

Returns the mean accuracy on the given test data and labels.

Parameters
Xarray-like, shape [n_samples, n_features]

Test samples

yarray-like, shape [n_samples, n_outputs]

True values for X

Returns
scoresfloat

accuracy_score of self.predict(X) versus y

set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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
selfobject

Estimator instance.