def METHOD_NAME(self):
    import snapml  # type: ignore
    clf = snapml.RandomForestClassifier()
    self.assertIsInstance(clf, snapml.RandomForestClassifier)
    fit_result = clf.fit(self.train_X, self.train_y)
    self.assertIsInstance(fit_result, snapml.RandomForestClassifier)
    for metric in [sklearn.metrics.accuracy_score, sklearn.metrics.roc_auc_score]:
        scorer = sklearn.metrics.make_scorer(metric)
        _ = scorer(clf, self.test_X, self.test_y)