class_docstr: 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 :ref:`User Guide <adaboost>`.
  
  .. versionadded:: 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 :class:`~sklearn.tree.DecisionTreeClassifier`
      initialized with `max_depth=1`.
  
      .. versionadded:: 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 :term:`Glossary <random_state>`.
  
  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 :class:`~sklearn.tree.DecisionTreeClassifier`
      initialized with `max_depth=1`.
  
      .. deprecated:: 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.
  
      .. versionadded:: 1.2
         `base_estimator_` was renamed to `estimator_`.
  
  base_estimator_ : estimator
      The base estimator from which the ensemble is grown.
  
      .. deprecated:: 1.2
          `base_estimator_` is deprecated and will be removed in 1.4.
          Use `estimator_` instead.
  
  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 if supported by the
      ``estimator`` (when based on decision trees).
  
      Warning: impurity-based feature importances can be misleading for
      high cardinality features (many unique values). See
      :func:`sklearn.inspection.permutation_importance` as an alternative.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 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] J. 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...
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docstr: 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.
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code: def staged_predict(self, 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.
      """
      X = self._check_X(X)
  
      n_classes = self.n_classes_
      classes = self.classes_
  
      if n_classes == 2:
          for pred in self.staged_decision_function(X):
              yield np.array(classes.take(pred > 0, axis=0))
  
      else:
          for pred in self.staged_decision_function(X):
              yield np.array(
                  classes.take(np.argmax(pred, axis=1), axis=0)
              )
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  from scipy.special import xlogy
  from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier, is_regressor
  from ..metrics import accuracy_score, r2_score
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..utils import _safe_indexing, check_random_state
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils.extmath import softmax, stable_cumsum
  from ..utils.validation import _check_sample_weight, _num_samples, check_is_fitted, has_fit_parameter
  from ._base import BaseEnsemble
label_desc: Use image node_id 7 for calling the AdaBoostClassifier obj's underlying member method code with example usage: obj.staged_predict(X) without return types