class_docstr: Bagging ensemble of time series classifiers.
  
  Fits ``n_estimators`` clones of a classifier on
  datasets which are instance sub-samples and/or variable sub-samples.
  
  On ``predict_proba``, the mean average of probabilistic predictions is returned.
  For a deterministic classifier, this results in majority vote for ``predict``.
  
  The estimator allows to choose sample sizes fir instances, variables,
  and whether sampling is with or without replacement.
  
  Direct generalization of ``sklearn``'s ``BaggingClassifier``
  to the time series classification task.
  
  Note: if n_features=1, BaggingClassifier turns a univariate classifier into
  a multivariate classifier (as slices seen by ``estimator`` are all univariate).
  
  Parameters
  ----------
  estimator : sktime classifier, descendant of BaseClassifier
      classifier to use in the bagging estimator
  n_estimators : int, default=10
      number of estimators in the sample for bagging
  n_samples : int or float, default=1.0
      The number of instances drawn from ``X`` in ``fit`` to train each clone
      If int, then indicates number of instances precisely
      If float, interpreted as a fraction, and rounded by ``ceil``
  n_features : int or float, default=1.0
      The number of features/variables drawn from ``X`` in ``fit`` to train each clone
      If int, then indicates number of instances precisely
      If float, interpreted as a fraction, and rounded by ``ceil``
      Note: if n_features=1, BaggingClassifier turns a univariate classifier into
      a multivariate classifier (as slices seen by ``estimator`` are all univariate).
  bootstrap : boolean, default=True
      whether samples/instances are drawn with replacement (True) or not (False)
  bootstrap_features : boolean, default=False
      whether features/variables are drawn with replacement (True) or not (False)
  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 of sktime classifiers
      clones of classifier in `estimator` fitted in the ensemble
  
  Examples
  --------
  >>> from sktime.classification.ensemble import BaggingClassifier
  >>> from sktime.classification.kernel_based import RocketClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train") # doctest: +SKIP
  >>> X_test, y_test = load_unit_test(split="test") # doctest: +SKIP
  >>> clf = BaggingClassifier(
  ...     RocketClassifier(num_kernels=100),
  ...     n_estimators=10,
  ... ) # doctest: +SKIP
  >>> clf.fit(X_train, y_train) # doctest: +SKIP
  BaggingClassifier(...)
  >>> y_pred = clf.predict(X_test) # doctest: +SKIP
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code: def __init__(
      self,
      estimator,
      n_estimators=10,
      n_samples=1.0,
      n_features=1.0,
      bootstrap=True,
      bootstrap_features=False,
      random_state=None,
  ):
      self.estimator = estimator
      self.n_estimators = n_estimators
      self.n_samples = n_samples
      self.n_features = n_features
      self.bootstrap = bootstrap
      self.bootstrap_features = bootstrap_features
      self.random_state = random_state
  
      super().__init__()
  
      if n_features == 1:
          # if n_features == 1, this turns a univariate classifier into multivariate
          tags_to_clone = ["capability:missing_values"]
      else:
          tags_to_clone = [
              "capability:multivariate",
              "capability:missing_values",
          ]
      self.clone_tags(estimator, tags_to_clone)
  
file_path: sktime/sktime/classification/ensemble/_bagging.py
imports: from math import ceil
  import numpy
  import pandas
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 1 to create a new BaggingClassifier object from inherited base classes: BaseClassifier with example: obj = BaggingClassifier(estimator, n_estimators, n_samples, n_features, bootstrap, bootstrap_features, random_state)