def _test_evaluation(self, allow_slow):
        """
        Test that the same predictions are made
        """

        # Generate some smallish (some kernels take too long on anything else) random data
        x, y = [], []
        for _ in range(50):
            cur_x1, cur_x2 = random.gauss(2, 3), random.gauss(-1, 2)
            x.append([cur_x1, cur_x2])
            y.append(1 + 2 * cur_x1 + 3 * cur_x2)

        input_names = ["x1", "x2"]
        df = pd.DataFrame(x, columns=input_names)

        # Parameters to test
        kernel_parameters = [
            {},
            {"kernel": "rbf", "gamma": 1.2},
            {"kernel": "linear"},
            {"kernel": "poly"},
            {"kernel": "poly", "degree": 2},
            {"kernel": "poly", "gamma": 0.75},
            {"kernel": "poly", "degree": 0, "gamma": 0.9, "coef0": 2},
            {"kernel": "sigmoid"},
            {"kernel": "sigmoid", "gamma": 1.3},
            {"kernel": "sigmoid", "coef0": 0.8},
            {"kernel": "sigmoid", "coef0": 0.8, "gamma": 0.5},
        ]
        non_kernel_parameters = [
            {},
            {"C": 1},
            {"C": 1.5, "shrinking": True},
            {"C": 0.5, "shrinking": False, "nu": 0.9},
        ]

        # Test
        for param1 in non_kernel_parameters:
            for param2 in kernel_parameters:
                cur_params = param1.copy()
                cur_params.update(param2)

                cur_model = NuSVR(**cur_params)
                cur_model.fit(x, y)
                df["target"] = cur_model.predict(x)

                spec = scikit_converter.convert(cur_model, input_names, "target")

                if _is_macos() and _macos_version() >= (10, 13):
                    metrics = evaluate_regressor(spec, df)
                    self.assertAlmostEqual(metrics["max_error"], 0)

                if not allow_slow:
                    break

            if not allow_slow:
                break

        Test that the same predictions are made
        
coremltools/test/sklearn_tests/test_NuSVR.py