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
  
      # 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 = 1 * (
          scikit_data["target"] > scikit_data["target"].mean()
      )
      self.feature_names = scikit_data.feature_names
      self.output_name = "target"
  
imports: import itertools
  import pytest
  import pandas
  import unittest
  from coremltools._deps import _HAS_SKLEARN, _HAS_XGBOOST
  from coremltools.models.utils import evaluate_classifier, evaluate_classifier_with_probabilities, _macos_version, _is_macos
label_desc: Use image node_id 1 for calling the BoostedTreeClassificationBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj.setUpClass() without return types