class_docstr: Base class for all ensemble classes.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
  
  Parameters
  ----------
  base_estimator : object, optional (default=None)
      The base estimator from which the ensemble is built.
  
  n_estimators : integer
      The number of estimators in the ensemble.
  
  estimator_params : list of strings
      The list of attributes to use as parameters when instantiating a
      new base estimator. If none are given, default parameters are used.
  
  Attributes
  ----------
  base_estimator_ : estimator
      The base estimator from which the ensemble is grown.
  
  estimators_ : list of estimators
      The collection of fitted base estimators.
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code: def __init__(
      self, base_estimator, n_estimators=10, estimator_params=tuple()
  ):
      # Set parameters
      self.base_estimator = base_estimator
      self.n_estimators = n_estimators
      self.estimator_params = estimator_params
  
      # Don't instantiate estimators now! Parameters of base_estimator might
      # still change. Eg., when grid-searching with the nested object syntax.
      # This needs to be filled by the derived classes.
      self.estimators_ = []
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/ensemble/base.py
imports: import numpy
  import numbers
  from ..base import clone
  from ..base import BaseEstimator
  from ..base import MetaEstimatorMixin
  from ..utils import _get_n_jobs, check_random_state
label_desc: Use image node_id 1 to create a new BaseEnsemble object from inherited base classes: BaseEstimator, MetaEstimatorMixin with example: obj = BaseEnsemble(base_estimator, n_estimators, estimator_params)