class_docstr: Base class for AdaBoost estimators.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","AdaBoost","estimators",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
code: def __init__(
      self,
      base_estimator=None,
      n_estimators=50,
      estimator_params=tuple(),
      learning_rate=1.0,
      random_state=None,
  ):
      super(BaseWeightBoosting, self).__init__(
          base_estimator=base_estimator,
          n_estimators=n_estimators,
          estimator_params=estimator_params,
      )
  
      self.learning_rate = learning_rate
      self.random_state = random_state
  
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 1 to create a new BaseWeightBoosting object from inherited base classes:  with example: obj = BaseWeightBoosting(base_estimator, n_estimators, estimator_params, learning_rate, random_state)