class_docstr: Supervised Time Series Forest (STSF).
  
  An ensemble of decision trees built on intervals selected through a supervised
  process as described in _[1].
  Overview: Input n series length m
  For each tree
      - sample X using class-balanced bagging
      - sample intervals for all 3 representations and 7 features using supervised
      - method
      - find mean, median, std, slope, iqr, min and max using their corresponding
      - interval for each rperesentation, concatenate to form new data set
      - build decision tree on new data set
  Ensemble the trees with averaged probability estimates.
  
  Parameters
  ----------
  n_estimators : int, default=200
      Number of estimators to build for the ensemble.
  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 number generation.
  
  Attributes
  ----------
  n_classes_ : int
      The number of classes.
  n_instances_ : int
      The number of train cases.
  series_length_ : int
      The length of each series.
  classes_ : list
      The classes labels.
  intervals : array-like of shape [n_estimators][3][7][n_intervals][2]
      Stores indexes of all start and end points for all estimators. Each estimator
      contains indexes for each representation and feature combination.
  estimators_ : list of shape (n_estimators) of DecisionTreeClassifier
      The collections of estimators trained in fit.
  
  Notes
  -----
  For the Java version, see
  `TSML <https://github.com/uea-machine-learning/tsml/blob/master/src/main/
   java/tsml/classifiers/interval_based/STSF.java>`_.
  
  References
  ----------
  .. [1] Cabello, Nestor, et al. "Fast and Accurate Time Series Classification
     Through Supervised Interval Search." IEEE ICDM 2020
  
  Examples
  --------
  >>> from sktime.classification.interval_based import SupervisedTimeSeriesForest
  >>> 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 = SupervisedTimeSeriesForest(n_estimators=5)
  >>> clf.fit(X_train, y_train)
  SupervisedTimeSeriesForest(n_estimators=5)
  >>> 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`.
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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`.
      """
      if parameter_set == "results_comparison":
          return {"n_estimators": 10}
      else:
          return {"n_estimators": 2}
  
imports: import math
  import numpy
  from joblib import Parallel, delayed
  from scipy import signal, stats
  from sklearn.base import clone
  from sklearn.preprocessing import StandardScaler
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.utils.slope_and_trend import _slope
label_desc: Use image node_id 10 for calling the SupervisedTimeSeriesForest obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict, dict