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,
      estimator=None,
      *,
      n_estimators=50,
      estimator_params=tuple(),
      learning_rate=1.0,
      random_state=None,
      base_estimator="deprecated",
  ):
      super().__init__(
          estimator=estimator,
          n_estimators=n_estimators,
          estimator_params=estimator_params,
          base_estimator=base_estimator,
      )
  
      self.learning_rate = learning_rate
      self.random_state = random_state
  
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 1 to create a new BaseWeightBoosting object from inherited base classes: BaseEnsemble with example: obj = BaseWeightBoosting(estimator)