class_docstr: Unit test class for testing scikit-learn converter and running both models
code: def test_boston_housing_parameter_stress_test(self):
      ## These are all the options in decision tree regression of scikit-learn
      options = dict(
          criterion=["mse"],
          n_estimators=[1, 5, 10],
          max_depth=[1, 5],
          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],
          min_impurity_decrease=[1e-07, 0.1, 0.0],
      )
  
      # Make a cartesian product of all options
      import itertools
  
      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)
  
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 6 for calling the RandomForestRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj.test_boston_housing_parameter_stress_test() without return types