def test_linear_svr_evaluation(self):
        """
        Check that the evaluation results are the same in scikit learn and coremltools
        """
        ARGS = [
            {},
            {"C": 0.5, "epsilon": 0.25},
            {"dual": False, "loss": "squared_epsilon_insensitive"},
            {"tol": 0.005},
            {"fit_intercept": False},
            {"intercept_scaling": 1.5},
        ]

        input_names = self.scikit_data["feature_names"]
        df = pd.DataFrame(self.scikit_data["data"], columns=input_names)

        for cur_args in ARGS:
            cur_model = LinearSVR(**cur_args)
            cur_model.fit(self.scikit_data["data"], self.scikit_data["target"])
            spec = convert(cur_model, input_names, "target")

            df["target"] = cur_model.predict(self.scikit_data["data"])

            metrics = evaluate_regressor(spec, df)
            self.assertAlmostEqual(metrics["max_error"], 0)

        Check that the evaluation results are the same in scikit learn and coremltools
        
coremltools/test/sklearn_tests/test_linear_regression.py