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.
        SummaryClassifier provides the following special sets:
             "results_comparison" - used in some classifiers to compare against
                previously generated results where the default set of parameters
                cannot produce suitable probability estimates
    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"),
        }