class_docstr: Summary statistic classifier.
  
  This classifier simply transforms the input data using the SummaryTransformer
  transformer and builds a provided estimator using the transformed data.
  
  Parameters
  ----------
  summary_functions : str, list, tuple, default=("mean", "std", "min", "max")
      Either a string, or list or tuple of strings indicating the pandas
      summary functions that are used to summarize each column of the dataset.
      Must be one of ("mean", "min", "max", "median", "sum", "skew", "kurt",
      "var", "std", "mad", "sem", "nunique", "count").
  summary_quantiles : str, list, tuple or None, default=(0.25, 0.5, 0.75)
      Optional list of series quantiles to calculate. If None, no quantiles
      are calculated.
  estimator : sklearn classifier, default=None
      An sklearn estimator to be built using the transformed data. Defaults to a
      Random Forest with 200 trees.
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int or None, default=None
      Seed for random, integer.
  
  Attributes
  ----------
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes)
      Holds the label for each class.
  
  See Also
  --------
  SummaryTransformer
  
  Examples
  --------
  >>> from sktime.classification.feature_based import SummaryClassifier
  >>> from sklearn.ensemble import RandomForestClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
  >>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
  >>> clf = SummaryClassifier(estimator=RandomForestClassifier(n_estimators=5))
  >>> clf.fit(X_train, y_train)
  SummaryClassifier(...)
  >>> y_pred = clf.predict(X_test)
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`.
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`.
      """
      if parameter_set == "results_comparison":
          return {"estimator": RandomForestClassifier(n_estimators=10)}
      else:
          return {
              "estimator": RandomForestClassifier(n_estimators=2),
              "summary_functions": ("mean", "min", "max"),
          }
  
file_path: sktime/sktime/classification/feature_based/_summary_classifier.py
imports: import numpy
  from sklearn.ensemble import RandomForestClassifier
  from sktime.base._base import _clone_estimator
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.series.summarize import SummaryTransformer
label_desc: Use image node_id 5 for calling the SummaryClassifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict, dict