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# `sklearn.multiclass`.OneVsRestClassifier

class sklearn.multiclass.OneVsRestClassifier(estimator, *, n_jobs=None, verbose=0)

  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.

  Read more in the User Guide.

  Parameters:
    estimator estimator object

      A regressor or a classifier that implements fit. When a classifier is passed, decision_function will be used in priority and it will fallback to predict_proba if it is not available. When a regressor is passed, predict is used.

    n_jobs int, default=None

      The number of jobs to use for the computation: the `n_classes` one-vs-rest problems are computed in parallel.

      `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

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

    verbose int, default=0

      The verbosity level, if non zero, progress messages are printed. Below 50, the output is sent to stderr. Otherwise, the output is sent to stdout. The frequency of the messages increases with the verbosity level, reporting all iterations at 10. See `joblib.Parallel` for more details.

      New in version 1.1.

  Attributes:
    estimators_list of `n_classes` estimators

      Estimators used for predictions.

    classes_array, shape = [`n_classes`]

      Class labels.

    `n_classes_`int

      Number of classes.

    label_binarizer_LabelBinarizer object

      Object used to transform multiclass labels to binary labels and vice-versa.

    `multilabel_`boolean

      Whether this is a multilabel classifier.

    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 estimator exposes such an attribute when fit.

      New in version 1.0.

  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])

  Methods

  `decision_function`(X) | Decision function for the OneVsRestClassifier.
  `fit`(X, y) | Fit underlying estimators.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `partial_fit`(X, y[, classes]) | Partially fit underlying estimators.
  `predict`(X) | Predict multi-class targets using underlying estimators.
  `predict_proba`(X) | Probability estimates.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_partial_fit_request`(*[, classes]) | Request metadata passed to the `partial_fit` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  decision_function(X)

    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:
      X array-like of shape (n_samples, n_features)

        Input data.

    Returns:
      T array-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 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.

    Returns:
      self object

        Instance of fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        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)

    Partially fit underlying estimators.

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

    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.

      classes array, 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.

    Returns:
      self object

        Instance of partially fitted estimator.

  predict(X)

    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)

    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:
      T array-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)

    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:
      X array-like of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True labels for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

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

  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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$') → OneVsRestClassifier

    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.

    New 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:
      classes str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `classes` parameter in `partial_fit`.

    Returns:
      self object

        The updated object.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OneVsRestClassifier

    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.

    New 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_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.multiclass.OneVsRestClassifier`

Multilabel classification

Multilabel classification

Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)

Precision-Recall

Precision-Recall

Classifier Chain

Classifier Chain