class_docstr: Base class for Bagging meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","Bagging","meta-estimator",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
code: def __init__(
      self,
      estimator=None,
      n_estimators=10,
      *,
      max_samples=1.0,
      max_features=1.0,
      bootstrap=True,
      bootstrap_features=False,
      oob_score=False,
      warm_start=False,
      n_jobs=None,
      random_state=None,
      verbose=0,
      base_estimator="deprecated",
  ):
      super().__init__(
          estimator=estimator,
          n_estimators=n_estimators,
          base_estimator=base_estimator,
      )
      self.max_samples = max_samples
      self.max_features = max_features
      self.bootstrap = bootstrap
      self.bootstrap_features = bootstrap_features
      self.oob_score = oob_score
      self.warm_start = warm_start
      self.n_jobs = n_jobs
      self.random_state = random_state
      self.verbose = verbose
  
imports: import itertools
  import numbers
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from numbers import Integral
  from warnings import warn
  import numpy
  from ..base import ClassifierMixin, RegressorMixin, _fit_context
  from ..metrics import accuracy_score, r2_score
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..utils import check_random_state, column_or_1d, indices_to_mask
  from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
  from ..utils._tags import _safe_tags
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import check_classification_targets
  from ..utils.parallel import Parallel, delayed
  from ..utils.random import sample_without_replacement
  from ..utils.validation import _check_sample_weight, check_is_fitted, has_fit_parameter
  from ._base import BaseEnsemble, _partition_estimators
label_desc: Use image node_id 1 to create a new BaseBagging object from inherited base classes: BaseEnsemble with example: obj = BaseBagging(estimator, n_estimators)