class_docstr: Random Interval Classifier.
  
  This classifier simply transforms the input data using the RandomIntervals
  transformer and builds a provided estimator using the transformed data.
  
  Parameters
  ----------
  n_intervals : int, default=100,
      The number of intervals of random length, position and dimension to be
      extracted.
  interval_transformers : transformer or list of transformers, default=None,
      Transformer(s) used to extract features from each interval. If None, defaults to
      the Catch22 transformer.
  estimator : sklearn classifier, default=None
      An sklearn estimator to be built using the transformed data. Defaults to a
      Rotation 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
  --------
  RandomIntervals
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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 sklearn.ensemble import RandomForestClassifier
  
      from sktime.transformations.series.summarize import (
          SummaryTransformer,
      )
  
      if parameter_set == "results_comparison":
          return {
              "n_intervals": 3,
              "estimator": RandomForestClassifier(n_estimators=10),
              "interval_transformers": SummaryTransformer(
                  summary_function=("mean", "std", "min", "max"),
                  quantiles=(0.25, 0.5, 0.75),
              ),
          }
      else:
          return {
              "n_intervals": 2,
              "estimator": RandomForestClassifier(n_estimators=2),
              "interval_transformers": SummaryTransformer(
                  summary_function=("mean", "min", "max"),
              ),
          }
  
file_path: sktime/sktime/classification/feature_based/_random_interval_classifier.py
imports: import numpy
  from sktime.base._base import _clone_estimator
  from sktime.classification.base import BaseClassifier
  from sktime.classification.sklearn import RotationForest
  from sktime.transformations.panel.catch22 import Catch22
  from sktime.transformations.panel.random_intervals import RandomIntervals
label_desc: Use image node_id 5 for calling the RandomIntervalClassifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict, dict