class_docstr: Unit test class for testing scikit-learn converter and running both models
class_docstr_tok: ["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
      from sklearn.tree import DecisionTreeRegressor
  
      # Load data and train model
      scikit_data = load_boston()
      self.scikit_data = scikit_data
      self.X = scikit_data["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/xgboost_tests/test_decision_tree_regression_numeric.py
imports: import unittest
  from coremltools.converters import sklearn
  from coremltools.models.utils import evaluate_regressor
  import pandas
  import os
  from coremltools.models.utils import evaluate_regressor, _macos_version, _is_macos
  from coremltools._deps import _HAS_SKLEARN
  import pytest
label_desc: Use image node_id 1 for calling the DecisionTreeRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj.setUpClass() without return types