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# `sklearn.multiclass`.OutputCodeClassifier

class sklearn.multiclass.OutputCodeClassifier(estimator, *, code_size=1.5, random_state=None, n_jobs=None)

  (Error-Correcting) Output-Code multiclass strategy.

  Output-code based strategies consist in representing each class with a binary code (an array of 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At prediction time, the classifiers are used to project new points in the class space and the class closest to the points is chosen. The main advantage of these strategies is that the number of classifiers used can be controlled by the user, either for compressing the model (0 < `code_size` < 1) or for making the model more robust to errors (`code_size` > 1). See the documentation for more details.

  Read more in the User Guide.

  Parameters:
    estimator estimator object

      An estimator object implementing fit and one of decision_function or predict_proba.

    code_size float, default=1.5

      Percentage of the number of classes to be used to create the code book. A number between 0 and 1 will require fewer classifiers than one-vs-the-rest. A number greater than 1 will require more classifiers than one-vs-the-rest.

    random_state int, RandomState instance, default=None

      The generator used to initialize the codebook. Pass an int for reproducible output across multiple function calls. See Glossary.

    n_jobs int, default=None

      The number of jobs to use for the computation: the multiclass 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.

  Attributes:
    estimators_list of `int(n_classes * code_size)` estimators

      Estimators used for predictions.

    classes_ndarray of shape (n_classes,)

      Array containing labels.

    code_book_ndarray of shape (n_classes, code_size)

      Binary array containing the code of each class.

    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

  `OneVsRestClassifier`

    One-vs-all multiclass strategy.

  `OneVsOneClassifier`

    One-vs-one multiclass strategy.

  References

  [1]

  “Solving multiclass learning problems via error-correcting output codes”, Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995.

  [2]

  “The error coding method and PICTs”, James G., Hastie T., Journal of Computational and Graphical statistics 7, 1998.

  [3]

  “The Elements of Statistical Learning”, Hastie T., Tibshirani R., Friedman J., page 606 (second-edition) 2008.

  Examples

      >>> from sklearn.multiclass import OutputCodeClassifier
      >>> from sklearn.ensemble import RandomForestClassifier
      >>> from sklearn.datasets import make_classification
      >>> X, y = make_classification(n_samples=100, n_features=4,
      ...                            n_informative=2, n_redundant=0,
      ...                            random_state=0, shuffle=False)
      >>> clf = OutputCodeClassifier(
      ...     estimator=RandomForestClassifier(random_state=0),
      ...     random_state=0).fit(X, y)
      >>> clf.predict([[0, 0, 0, 0]])
      array([1])

  Methods

  `fit`(X, y) | Fit underlying estimators.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict multi-class targets using underlying estimators.
  `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_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y)

    Fit underlying estimators.

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

        Data.

      y array-like of shape (n_samples,)

        Multi-class targets.

    Returns:
      self object

        Returns a fitted instance of self.

  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.

  predict(X)

    Predict multi-class targets using underlying estimators.

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

        Data.

    Returns:
      y ndarray of shape (n_samples,)

        Predicted multi-class targets.

  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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OutputCodeClassifier

    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.