class_docstr: Online Updating Ensemble of forecasters.
  
  Parameters
  ----------
  ensemble_algorithm : ensemble algorithm
  forecasters : list of (str, estimator) tuples
  n_jobs : int or None, optional (default=None)
      The number of jobs to run in parallel for fit. None means 1 unless
      in a joblib.parallel_backend context.
      -1 means using all processors.
class_docstr_tok: ["Online","Updating","Ensemble","of","forecasters",".","Parameters","--","--","--","--","--","ensemble_algorithm",":","ensemble","algorithm","forecasters",":","list","of","(","str",",","estimator",")","tuples","n_jobs",":","int","or","None",",","optional","(","default=None",")","The","number","of","jobs","to","run","in","parallel","for","fit",".","None","means","1","unless","in","a","joblib.parallel_backend","context",".","-1","means","using","all","processors","."]
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.
  
  
  Returns
  -------
  params : dict or list of dict
docstr_tok: ["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",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict"]
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.
  
  
      Returns
      -------
      params : dict or list of dict
      """
      from sktime.forecasting.naive import NaiveForecaster
  
      FORECASTER = NaiveForecaster()
      params = {"forecasters": [("f1", FORECASTER), ("f2", FORECASTER)]}
      return params
  
file_path: sktime/sktime/forecasting/online_learning/_online_ensemble.py
imports: import numpy
  import pandas
  from sktime.forecasting.compose._ensemble import EnsembleForecaster
label_desc: Use image node_id 6 for calling the OnlineEnsembleForecaster obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params