class_docstr: Unit test class for testing scikit-learn converter and running both models
docstr: Set up the unit test by loading the dataset and training a model.
docstr_tok: ["Set","up","the","unit","test","by","loading","the","dataset","and","training","a","model","."]
code: def setUpClass(self):
      """
      Set up the unit test by loading the dataset and training a model.
      """
      from sklearn.datasets import load_boston
  
      # Load data and train model
      scikit_data = load_boston()
      self.scikit_data = scikit_data
      self.X = scikit_data.data.astype("f").astype(
          "d"
      )  ## scikit-learn downcasts data
      self.target = scikit_data.target
      self.feature_names = scikit_data.feature_names
      self.output_name = "target"
  
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 1 for calling the RandomForestRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj.setUpClass() without return types