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# `sklearn.ensemble`.AdaBoostRegressor

class sklearn.ensemble.AdaBoostRegressor(estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None, base_estimator='deprecated')

  An AdaBoost regressor.

  An AdaBoost [1] regressor is a meta-estimator that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction. As such, subsequent regressors focus more on difficult cases.

  This class implements the algorithm known as AdaBoost.R2 [2].

  Read more in the User Guide.

  New in version 0.14.

  Parameters:
    estimator object, default=None

      The base estimator from which the boosted ensemble is built. If `None`, then the base estimator is `DecisionTreeRegressor` initialized with `max_depth=3`.

      New in version 1.2: `base_estimator` was renamed to `estimator`.

    n_estimators int, default=50

      The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. Values must be in the range `[1, inf)`.

    learning_rate float, default=1.0

      Weight applied to each regressor at each boosting iteration. A higher learning rate increases the contribution of each regressor. There is a trade-off between the `learning_rate` and `n_estimators` parameters. Values must be in the range `(0.0, inf)`.

    loss{‘linear’, ‘square’, ‘exponential’}, default=’linear’

      The loss function to use when updating the weights after each boosting iteration.

    random_state int, RandomState instance or None, default=None

      Controls the random seed given at each `estimator` at each boosting iteration. Thus, it is only used when `estimator` exposes a `random_state`. In addition, it controls the bootstrap of the weights used to train the `estimator` at each boosting iteration. Pass an int for reproducible output across multiple function calls. See Glossary.

    base_estimator object, default=None

      The base estimator from which the boosted ensemble is built. If `None`, then the base estimator is `DecisionTreeRegressor` initialized with `max_depth=3`.

      Deprecated since version 1.2: `base_estimator` is deprecated and will be removed in 1.4. Use `estimator` instead.

  Attributes:
    estimator_estimator

      The base estimator from which the ensemble is grown.

      New in version 1.2: `base_estimator_` was renamed to `estimator_`.

    `base_estimator_`estimator

      Estimator used to grow the ensemble.

    estimators_list of regressors

      The collection of fitted sub-estimators.

    estimator_weights_ndarray of floats

      Weights for each estimator in the boosted ensemble.

    estimator_errors_ndarray of floats

      Regression error for each estimator in the boosted ensemble.

    `feature_importances_`ndarray of shape (n_features,)

      The impurity-based feature importances.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `AdaBoostClassifier`

    An AdaBoost classifier.

  `GradientBoostingRegressor`

    Gradient Boosting Classification Tree.

  `sklearn.tree.DecisionTreeRegressor`

    A decision tree regressor.

  References

  [1]

  Y. Freund, R. Schapire, “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting”, 1995.

  [2]

  8. Drucker, “Improving Regressors using Boosting Techniques”, 1997.

  Examples

      >>> from sklearn.ensemble import AdaBoostRegressor
      >>> from sklearn.datasets import make_regression
      >>> X, y = make_regression(n_features=4, n_informative=2,
      ...                        random_state=0, shuffle=False)
      >>> regr = AdaBoostRegressor(random_state=0, n_estimators=100)
      >>> regr.fit(X, y)
      AdaBoostRegressor(n_estimators=100, random_state=0)
      >>> regr.predict([[0, 0, 0, 0]])
      array([4.7972...])
      >>> regr.score(X, y)
      0.9771...

  Methods

  `fit`(X, y[, sample_weight]) | Build a boosted classifier/regressor from the training set (X, y).
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict regression value for X.
  `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_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.
  `staged_predict`(X) | Return staged predictions for X.
  `staged_score`(X, y[, sample_weight]) | Return staged scores for X, y.

  property base_estimator_

    Estimator used to grow the ensemble.

  property feature_importances_

    The impurity-based feature importances.

    The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.

    Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See `sklearn.inspection.permutation_importance` as an alternative.

    Returns:
      feature_importances_ndarray of shape (n_features,)

        The feature importances.

  fit(X, y, sample_weight=None)

    Build a boosted classifier/regressor from the training set (X, y).

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

        The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

      y array-like of shape (n_samples,)

        The target values.

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

        Sample weights. If None, the sample weights are initialized to 1 / n_samples.

    Returns:
      self object

        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.

  predict(X)

    Predict regression value for X.

    The predicted regression value of an input sample is computed as the weighted median prediction of the regressors in the ensemble.

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

        The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

    Returns:
      y ndarray of shape (n_samples,)

        The predicted regression values.

  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 = '$UNCHANGED$') → AdaBoostRegressor

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

    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.

  staged_predict(X)

    Return staged predictions for X.

    The predicted regression value of an input sample is computed as the weighted median prediction of the regressors in the ensemble.

    This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine the prediction on a test set after each boost.

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

        The training input samples.

    Yields:
      y generator of ndarray of shape (n_samples,)

        The predicted regression values.

  staged_score(X, y, sample_weight=None)

    Return staged scores for X, y.

    This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost.

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

        The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

      y array-like of shape (n_samples,)

        Labels for X.

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

        Sample weights.

    Yields:
      z float

## Examples using `sklearn.ensemble.AdaBoostRegressor`

Decision Tree Regression with AdaBoost

Decision Tree Regression with AdaBoost