code: def test_binary_classifier_stress_test(self):
      options = dict(
          n_estimators=[1, 5, 10],
          max_depth=[1, 5, None],
          min_samples_split=[2, 10, 0.5],
          min_samples_leaf=[1, 5],
          min_weight_fraction_leaf=[0.0, 0.5],
          max_leaf_nodes=[None, 20],
      )
  
      if _SKLEARN_VERSION >= StrictVersion("0.19"):
          options["min_impurity_decrease"] = [1e-07, 0.1]
  
      # Make a cartesian product of all options
      product = itertools.product(*options.values())
      args = [dict(zip(options.keys(), p)) for p in product]
  
      print(
          "Testing a total of %s cases. This could take a while"
          % len(args)
      )
      for it, arg in enumerate(args):
          self._train_convert_evaluate_assert(**arg)
  
imports: import unittest
  import itertools
  import os
  import pandas
  import numpy
  from coremltools._deps import _HAS_SKLEARN, _SKLEARN_VERSION
  from coremltools.models.utils import evaluate_classifier, _macos_version, _is_macos
  from distutils.version import StrictVersion
  import pytest
label_desc: Use image node_id 3 for calling the RandomForestBinaryClassifierBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj.test_binary_classifier_stress_test() without return types