class_docstr: Unit test class for testing scikit-learn converter and running both models
docstr: Check the metrics
docstr_tok: ["Check","the","metrics"]
code: def _check_metrics(self, metrics, params={}):
      """
      Check the metrics
      """
      self.assertAlmostEqual(
          metrics["rmse"],
          0,
          delta=1e-5,
          msg="Failed case %s. Results %s" % (params, metrics),
      )
      self.assertAlmostEqual(
          metrics["max_error"],
          0,
          delta=1e-5,
          msg="Failed case %s. Results %s" % (params, metrics),
      )
  
file_path: coremltools/coremltools/test/xgboost_tests/test_decision_tree_regression_numeric.py
imports: import itertools
  import unittest
  from distutils.version import StrictVersion
  import pandas
  import pytest
  from coremltools._deps import _HAS_SKLEARN, _SKLEARN_VERSION
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_regressor
label_desc: Use image node_id 2 for calling the DecisionTreeRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj._check_metrics(metrics, params) without return types