Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.multioutput`.MultiOutputRegressor

class sklearn.multioutput.MultiOutputRegressor(estimator, *, n_jobs=None)

  Multi target regression.

  This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.

  New in version 0.18.

  Parameters:
    estimator estimator object

      An estimator object implementing fit and predict.

    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:
    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

  `RegressorChain`

    A multi-label model that arranges regressions into a chain.

  `MultiOutputClassifier`

    Classifies each output independently rather than chaining.

  Examples

      >>> import numpy as np
      >>> from sklearn.datasets import load_linnerud
      >>> from sklearn.multioutput import MultiOutputRegressor
      >>> from sklearn.linear_model import Ridge
      >>> X, y = load_linnerud(return_X_y=True)
      >>> regr = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
      >>> regr.predict(X[[0]])
      array([[176..., 35..., 57...]])

  Methods

  `fit`(X, y[, sample_weight]) | Fit the model to data, separately for each output variable.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `partial_fit`(X, y[, sample_weight]) | Incrementally fit the model to data, for each output variable.
  `predict`(X) | Predict multi-output variable using model for each target variable.
  `score`(X, y[, sample_weight]) | Return the coefficient of determination of the prediction.
  `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`(*[, sample_weight]) | Request metadata passed to the `partial_fit` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y, sample_weight=None, **fit_params)

    Fit the model to data, separately for each output variable.

    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. An indicator matrix turns on multilabel estimation.

      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.

      **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, sample_weight=None, **partial_fit_params)

    Incrementally fit the model to data, for each output variable.

    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.

      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.

  score(X, y, sample_weight=None)

    Return the coefficient of determination of the prediction.

    The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares `((y_true - y_pred)** 2).sum()` and \(v\) is the total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of `y`, disregarding the input features, would get a \(R^2\) score of 0.0.

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

        Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.

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

        True values for `X`.

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

        Sample weights.

    Returns:
      score float

        \(R^2\) of `self.predict(X)` w.r.t. `y`.

    Notes

    The \(R^2\) score used when calling `score` on a regressor uses `multioutput='uniform_average'` from version 0.23 to keep consistent with default value of `r2_score`. This influences the `score` method of all the multioutput regressors (except for `MultiOutputRegressor`).

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

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

    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:
      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.

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

    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.multioutput.MultiOutputRegressor`

Comparing random forests and the multi-output meta estimator

Comparing random forests and the multi-output meta estimator