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# `sklearn.ensemble`.AdaBoostClassifier

class sklearn.ensemble.AdaBoostClassifier(estimator=None, *, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None, base_estimator='deprecated')

  An AdaBoost classifier.

  An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

  This class implements the algorithm known as AdaBoost-SAMME [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. Support for sample weighting is required, as well as proper `classes_` and `n_classes_` attributes. If `None`, then the base estimator is `DecisionTreeClassifier` initialized with `max_depth=1`.

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

    algorithm{‘SAMME’, ‘SAMME.R’}, default=’SAMME.R’

      If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. `estimator` must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.

    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`. 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. Support for sample weighting is required, as well as proper `classes_` and `n_classes_` attributes. If `None`, then the base estimator is `DecisionTreeClassifier` initialized with `max_depth=1`.

      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 classifiers

      The collection of fitted sub-estimators.

    classes_ndarray of shape (n_classes,)

      The classes labels.

    n_classes_int

      The number of classes.

    estimator_weights_ndarray of floats

      Weights for each estimator in the boosted ensemble.

    estimator_errors_ndarray of floats

      Classification 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

  `AdaBoostRegressor`

    An AdaBoost regressor 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.

  `GradientBoostingClassifier`

    GB builds an additive model in a forward stage-wise fashion. Regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.

  `sklearn.tree.DecisionTreeClassifier`

    A non-parametric supervised learning method used for classification. Creates a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

  References

  [1]

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

  [2]

  10. Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class AdaBoost”, 2009.

  Examples

      >>> from sklearn.ensemble import AdaBoostClassifier
      >>> from sklearn.datasets import make_classification
      >>> X, y = make_classification(n_samples=1000, n_features=4,
      ...                            n_informative=2, n_redundant=0,
      ...                            random_state=0, shuffle=False)
      >>> clf = AdaBoostClassifier(n_estimators=100, random_state=0)
      >>> clf.fit(X, y)
      AdaBoostClassifier(n_estimators=100, random_state=0)
      >>> clf.predict([[0, 0, 0, 0]])
      array([1])
      >>> clf.score(X, y)
      0.983...

  Methods

  `decision_function`(X) | Compute the decision function of `X`.
  `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 classes for X.
  `predict_log_proba`(X) | Predict class log-probabilities for X.
  `predict_proba`(X) | Predict class probabilities for X.
  `score`(X, y[, sample_weight]) | 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_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.
  `staged_decision_function`(X) | Compute decision function of `X` for each boosting iteration.
  `staged_predict`(X) | Return staged predictions for X.
  `staged_predict_proba`(X) | Predict class probabilities for X.
  `staged_score`(X, y[, sample_weight]) | Return staged scores for X, y.

  property base_estimator_

    Estimator used to grow the ensemble.

  decision_function(X)

    Compute the decision function of `X`.

    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:
      score ndarray of shape of (n_samples, k)

        The decision function of the input samples. The order of outputs is the same as that of the classes_ attribute. Binary classification is a special cases with `k == 1`, otherwise `k==n_classes`. For binary classification, values closer to -1 or 1 mean more like the first or second class in `classes_`, respectively.

  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 classes for X.

    The predicted class of an input sample is computed as the weighted mean prediction of the classifiers 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 classes.

  predict_log_proba(X)

    Predict class log-probabilities for X.

    The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers 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:
      p ndarray of shape (n_samples, n_classes)

        The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

  predict_proba(X)

    Predict class probabilities for X.

    The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers 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:
      p ndarray of shape (n_samples, n_classes)

        The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

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

    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 = '$UNCHANGED$') → AdaBoostClassifier

    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_decision_function(X)

    Compute decision function of `X` for each boosting iteration.

    This method allows monitoring (i.e. determine error on testing set) after each boosting iteration.

    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.

    Yields:
      score generator of ndarray of shape (n_samples, k)

        The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with `k == 1`, otherwise `k==n_classes`. For binary classification, values closer to -1 or 1 mean more like the first or second class in `classes_`, respectively.

  staged_predict(X)

    Return staged predictions for X.

    The predicted class of an input sample is computed as the weighted mean prediction of the classifiers 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 of shape (n_samples, n_features)

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

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

        The predicted classes.

  staged_predict_proba(X)

    Predict class probabilities for X.

    The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

    This generator method yields the ensemble predicted class probabilities after each iteration of boosting and therefore allows monitoring, such as to determine the predicted class probabilities 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.

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

        The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

  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.AdaBoostClassifier`

Classifier comparison

Classifier comparison

Discrete versus Real AdaBoost

Discrete versus Real AdaBoost

Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees

Plot the decision surfaces of ensembles of trees on the iris dataset

Plot the decision surfaces of ensembles of trees on the iris dataset

Two-class AdaBoost

Two-class AdaBoost