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# `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:
    estimator estimator object

      An estimator object implementing fit and predict. A predict_proba method will be exposed only if `estimator` implements it.

    n_jobs int 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_metadata_routing`() | Get metadata routing of this object.
  `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_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_partial_fit_request`(*[, classes, ...]) | Request metadata passed to the `partial_fit` method.

  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.

      Y array-like of shape (n_samples, n_classes)

        The target values.

      sample_weight array-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_params dict of string -> object

        Parameters passed to the `estimator.fit` method of each step.

        New in version 0.23.

    Returns:
      self object

        Returns a fitted instance.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    New in version 1.3.

    Returns:
      routing MetadataRouter

        A `MetadataRouter` 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.

  partial_fit(X, y, classes=None, sample_weight=None, **partial_fit_params)

    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.

      classes list 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_weight array-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.

      **partial_fit_params dict of str -> object

        Parameters passed to the `estimator.partial_fit` method of each sub-estimator.

        Only available if `enable_metadata_routing=True`. See the User Guide.

        New in version 1.3.

    Returns:
      self object

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

        The input data.

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

        Test samples.

      y array-like of shape (n_samples, n_outputs)

        True values for X.

    Returns:
      scores float

        Mean accuracy of predicted target versus true target.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MultiOutputClassifier

    Request metadata passed to the `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 `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 `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:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

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

    Returns:
      self object

        The updated object.

  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 = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → MultiOutputClassifier

    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`.

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

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

    Returns:
      self object

        The updated object.