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"],
          splitter=["best"],
          max_depth=[1, 10, None],
          min_samples_split=[2, 10, 0.5],
          min_samples_leaf=[1, 5],
          min_weight_fraction_leaf=[0.0, 0.5],
          max_features=[None, 1, 5],
          max_leaf_nodes=[None, 20],
          min_impurity_decrease=[0.0, 1e-07, 0.1],
      )
      if _SKLEARN_VERSION < StrictVersion(
          "0.22"
      ):  # 'presort' option deprecated >=0.22
          options["presort"] = [False, True]
  
      # 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)
  
file_path: coremltools/coremltools/test/xgboost_tests/test_decision_tree_regression_numeric.py
imports: import itertools
  import unittest
  from distutils.version import StrictVersion
  import pandas
  import pytest
  from coremltools._deps import _HAS_SKLEARN, _SKLEARN_VERSION
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_regressor
label_desc: Use image node_id 5 for calling the DecisionTreeRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj.test_boston_housing_parameter_stress_test() without return types