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>`.
  
  Parameters
  ----------
  base_estimator : object, optional (default=DecisionTreeClassifier)
      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.
  
  n_estimators : integer, optional (default=50)
      The maximum number of estimators at which boosting is terminated.
      In case of perfect fit, the learning procedure is stopped early.
  
  learning_rate : float, optional (default=1.)
      Learning rate shrinks the contribution of each classifier by
      ``learning_rate``. There is a trade-off between ``learning_rate`` and
      ``n_estimators``.
  
  algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R')
      If 'SAMME.R' then use the SAMME.R real boosting algorithm.
      ``base_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, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  Attributes
  ----------
  estimators_ : list of classifiers
      The collection of fitted sub-estimators.
  
  classes_ : array of shape = [n_classes]
      The classes labels.
  
  n_classes_ : int
      The number of classes.
  
  estimator_weights_ : array of floats
      Weights for each estimator in the boosted ensemble.
  
  estimator_errors_ : array of floats
      Classification error for each estimator in the boosted
      ensemble.
  
  feature_importances_ : array of shape = [n_features]
      The feature importances if supported by the ``base_estimator``.
  
  See also
  --------
  AdaBoostRegressor, GradientBoostingClassifier, DecisionTreeClassifier
  
  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.
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docstr: Check the estimator and set the base_estimator_ attribute.
docstr_tok: ["Check","the","estimator","and","set","the","base_estimator_","attribute","."]
code: def _validate_estimator(self):
      """Check the estimator and set the base_estimator_ attribute."""
      super(AdaBoostClassifier, self)._validate_estimator(
          default=DecisionTreeClassifier(max_depth=1)
      )
  
      #  SAMME-R requires predict_proba-enabled base estimators
      if self.algorithm == "SAMME.R":
          if not hasattr(self.base_estimator_, "predict_proba"):
              raise TypeError(
                  "AdaBoostClassifier with algorithm='SAMME.R' requires "
                  "that the weak learner supports the calculation of class "
                  "probabilities with a predict_proba method.\n"
                  "Please change the base estimator or set "
                  "algorithm='SAMME' instead."
              )
      if not has_fit_parameter(self.base_estimator_, "sample_weight"):
          raise ValueError(
              "%s doesn't support sample_weight."
              % self.base_estimator_.__class__.__name__
          )
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from numpy.core.umath_tests import inner1d
  from .base import BaseEnsemble
  from ..base import ClassifierMixin, RegressorMixin, is_regressor
  from ..externals import six
  from ..externals.six.moves import zip
  from ..externals.six.moves import xrange
  from .forest import BaseForest
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..tree.tree import BaseDecisionTree
  from ..tree._tree import DTYPE
  from ..utils import check_array, check_X_y, check_random_state
  from ..metrics import accuracy_score, r2_score
  from sklearn.utils.validation import has_fit_parameter, check_is_fitted
label_desc: Use image node_id 3 for calling the AdaBoostClassifier obj's underlying member method code with example usage: obj._validate_estimator() without return types