OneVsRestClassifier#

class sklearn.multiclass.OneVsRestClassifier(estimator, *, n_jobs=None, verbose=0)[source]#

One-vs-the-rest (OvR) multiclass strategy.

Also known as one-vs-all, this strategy consists in fitting one classifier per class. For each classifier, the class is fitted against all the other classes. In addition to its computational efficiency (only n_classes classifiers are needed), one advantage of this approach is its interpretability. Since each class is represented by one and one classifier only, it is possible to gain knowledge about the class by inspecting its corresponding classifier. This is the most commonly used strategy for multiclass classification and is a fair default choice.

OneVsRestClassifier can also be used for multilabel classification. To use this feature, provide an indicator matrix for the target y when calling .fit. In other words, the target labels should be formatted as a 2D binary (0/1) matrix, where [i, j] == 1 indicates the presence of label j in sample i. This estimator uses the binary relevance method to perform multilabel classification, which involves training one binary classifier independently for each label.

See also

OneVsOneClassifier: One-vs-one multiclass strategy.
OutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
sklearn.multioutput.MultiOutputClassifier: Alternate way of extending an estimator for multilabel classification.
sklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables to binary indicator matrix.

Examples

>>> import numpy as np
>>> from sklearn.multiclass import OneVsRestClassifier
>>> from sklearn.svm import SVC
>>> X = np.array([
...     [10, 10],
...     [8, 10],
...     [-5, 5.5],
...     [-5.4, 5.5],
...     [-20, -20],
...     [-15, -20]
... ])
>>> y = np.array([0, 0, 1, 1, 2, 2])
>>> clf = OneVsRestClassifier(SVC()).fit(X, y)
>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])
array([2, 0, 1])

decision_function(X)[source]#

Decision function for the OneVsRestClassifier.

Return the distance of each sample from the decision boundary for each class. This can only be used with estimators which implement the decision_function method.

Parameters:

Xarray-like of shape (n_samples, n_features): Input data.

Returns:

Tarray-like of shape (n_samples, n_classes) or (n_samples,) for binary classification.: Result of calling decision_function on the final estimator.

Changed in version 0.19: output shape changed to (n_samples,) to conform to scikit-learn conventions for binary classification.

fit(X, y, **fit_params)[source]#

Fit underlying estimators.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Data.
y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes): Multi-class targets. An indicator matrix turns on multilabel classification.
**fit_paramsdict: Parameters passed to the estimator.fit method of each sub-estimator.

Added in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

Returns:

selfobject: Instance of fitted estimator.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.4.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

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.

property multilabel_#: Whether this is a multilabel classifier.

property n_classes_#: Number of classes.

partial_fit(X, y, classes=None, **partial_fit_params)[source]#

Partially fit underlying estimators.

Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iterations.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Data.
y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes): Multi-class targets. An indicator matrix turns on multilabel classification.
classesarray, shape (n_classes, ): Classes across all calls to partial_fit. Can be obtained via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is only required in the first call of partial_fit and can be omitted in the subsequent calls.
**partial_fit_paramsdict: Parameters passed to the estimator.partial_fit method of each sub-estimator.

Added in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

Returns:

selfobject: Instance of partially fitted estimator.

predict(X)[source]#

Predict multi-class targets using underlying estimators.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Data.

Returns:

y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes): Predicted multi-class targets.

predict_proba(X)[source]#

Probability estimates.

The returned estimates for all classes are ordered by label of classes.

Note that in the multilabel case, each sample can have any number of labels. This returns the marginal probability that the given sample has the label in question. For example, it is entirely consistent that two labels both have a 90% probability of applying to a given sample.

In the single label multiclass case, the rows of the returned matrix sum to 1.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Input data.

Returns:

Tarray-like of shape (n_samples, n_classes): Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

score(X, y, sample_weight=None)[source]#

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

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. y.

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.

set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$') 192; OneVsRestClassifier[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 routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to partial_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 to partial_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 the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for classes parameter in partial_fit.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') 192; OneVsRestClassifier[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
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 the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Gallery examples#

Plot classification probability

Multiclass sparse logistic regression on 20newgroups

Plot multinomial and One-vs-Rest Logistic Regression

Multilabel classification

Multiclass Receiver Operating Characteristic (ROC)

Precision-Recall

Overview of multiclass training meta-estimators

Multilabel classification using a classifier chain