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.assertAlmostEquals(
          metrics["rmse"],
          0.0,
          delta=1e-5,
          msg="Failed case %s. Results %s" % (params, metrics),
      )
      self.assertAlmostEquals(
          metrics["max_error"],
          0.0,
          delta=1e-5,
          msg="Failed case %s. Results %s" % (params, metrics),
      )
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/test/sklearn_tests/test_random_forest_regression_numeric.py
imports: import unittest
  import numpy
  import pandas
  import os
  from coremltools._deps import _HAS_SKLEARN
  from coremltools.models.utils import evaluate_regressor, _macos_version, _is_macos
  import pytest
label_desc: Use image node_id 2 for calling the RandomForestRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj._check_metrics(metrics, params) without return types