code: def _train_convert_evaluate_assert(self, **scikit_params):
      scikit_model = RandomForestClassifier(
          random_state=1, **scikit_params
      )
      scikit_model.fit(self.X, self.target)
  
      # Convert the model
      spec = skl_converter.convert(
          scikit_model, self.feature_names, self.output_name
      )
  
      if _is_macos() and _macos_version() >= (10, 13):
          # Get predictions
          df = pd.DataFrame(self.X, columns=self.feature_names)
          df["target"] = scikit_model.predict(self.X)
  
          # Evaluate it
          metrics = evaluate_classifier(spec, df, verbose=False)
          self._check_metrics(metrics, scikit_params)
  
imports: import itertools
  import unittest
  from distutils.version import StrictVersion
  import numpy
  import pandas
  import pytest
  from coremltools._deps import _HAS_SKLEARN, _SKLEARN_VERSION
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_classifier
label_desc: Use image node_id 2 for calling the RandomForestClassificationBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj._train_convert_evaluate_assert() without return types