class_docstr: Unit test class for testing scikit-learn converter and running both models
class_docstr_tok: ["Unit","test","class","for","testing","scikit-learn","converter","and","running","both","models"]
code: def _check_metrics(self, metrics, params={}):
      self.assertEqual(
          metrics["num_errors"],
          0,
          msg="Failed case %s. Results %s" % (params, metrics),
      )
  
imports: import itertools
  import unittest
  import numpy
  import pandas
  import pytest
  from coremltools._deps import _HAS_SKLEARN, _HAS_XGBOOST
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_classifier, evaluate_classifier_with_probabilities
label_desc: Use image node_id 2 for calling the BoostedTreeClassificationBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj._check_metrics(metrics, params) without return types