def METHOD_NAME(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"),
            ),
        }