class_docstr: Applies estimators to columns of an array or pandas DataFrame.
  
  This estimator allows different columns or column subsets of the input
  to be transformed separately and the features generated by each
  transformer will be ensembled to form a single output.
  
  Parameters
  ----------
  estimators : list of tuples
      List of (name, estimator, column(s)) tuples specifying the transformer
      objects to be applied to subsets of the data.
  
      name : string
          Like in Pipeline and FeatureUnion, this allows the
          transformer and its parameters to be set using ``set_params`` and searched
          in grid search.
      estimator :  or {'drop'}
          Estimator must support `fit` and `predict_proba`. Special-cased
          strings 'drop' and 'passthrough' are accepted as well, to
          indicate to drop the columns.
      column(s) : array-like of string or int, slice, boolean mask array or callable.
  
  remainder : {'drop', 'passthrough'} or estimator, default 'drop'
      By default, only the specified columns in `transformations` are
      transformed and combined in the output, and the non-specified
      columns are dropped. (default of ``'drop'``).
      By specifying ``remainder='passthrough'``, all remaining columns
      that were not specified in `transformations` will be automatically passed
      through. This subset of columns is concatenated with the output of
      the transformations.
      By setting ``remainder`` to be an estimator, the remaining
      non-specified columns will use the ``remainder`` estimator. The
      estimator must support `fit` and `transform`.
  
  Examples
  --------
  >>> from sktime.classification.dictionary_based import ContractableBOSS
  >>> from sktime.classification.interval_based import CanonicalIntervalForest
  >>> from sktime.datasets import load_basic_motions
  >>> X_train, y_train = load_basic_motions(split="train") # doctest: +SKIP
  >>> X_test, y_test = load_basic_motions(split="test") # doctest: +SKIP
  >>> cboss = ContractableBOSS(
  ...     n_parameter_samples=4, max_ensemble_size=2, random_state=0
  ... ) # doctest: +SKIP
  >>> cif = CanonicalIntervalForest(
  ...     n_estimators=2, n_intervals=4, att_subsample_size=4, random_state=0
  ... ) # doctest: +SKIP
  >>> estimators = [("cBOSS", cboss, 5), ("CIF", cif, [3, 4])] # doctest: +SKIP
  >>> col_ens = ColumnEnsembleClassifier(estimators=estimators) # doctest: +SKIP
  >>> col_ens.fit(X_train, y_train) # doctest: +SKIP
  ColumnEnsembleClassifier(...)
  >>> y_pred = col_ens.predict(X_test) # doctest: +SKIP
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docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      For classifiers, a "default" set of parameters should be provided for
      general testing, and a "results_comparison" set for comparing against
      previously recorded results if the general set does not produce suitable
      probabilities to compare against.
  
  Returns
  -------
  params : dict or list of dict, default={}
      Parameters to create testing instances of the class.
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`.
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          For classifiers, a "default" set of parameters should be provided for
          general testing, and a "results_comparison" set for comparing against
          previously recorded results if the general set does not produce suitable
          probabilities to compare against.
  
      Returns
      -------
      params : dict or list of dict, default={}
          Parameters to create testing instances of the class.
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`.
      """
      from sktime.classification.dictionary_based import (
          ContractableBOSS,
      )
      from sktime.classification.interval_based import (
          CanonicalIntervalForest,
      )
      from sktime.classification.interval_based import (
          TimeSeriesForestClassifier as TSFC,
      )
  
      if parameter_set == "results_comparison":
          cboss = ContractableBOSS(
              n_parameter_samples=4, max_ensemble_size=2, random_state=0
          )
          cif = CanonicalIntervalForest(
              n_estimators=2,
              n_intervals=4,
              att_subsample_size=4,
              random_state=0,
          )
          return {
              "estimators": [("cBOSS", cboss, 5), ("CIF", cif, [3, 4])]
          }
      else:
          return {
              "estimators": [
                  ("tsf1", TSFC(n_estimators=2), 0),
                  ("tsf2", TSFC(n_estimators=2), 0),
              ]
          }
  
imports: from itertools import chain
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from sktime.base import _HeterogenousMetaEstimator
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 2 for calling the ColumnEnsembleClassifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict, dict