def setUpClass(self):
        """
        Set up the unit test by loading the dataset and training a model.
        """
        scikit_data = load_boston()
        # n_estimators default changed >= 0.22. Specify explicitly to match <0.22 behavior.
        scikit_model = RandomForestClassifier(random_state=1, n_estimators=10)
        target = 1 * (scikit_data["target"] > scikit_data["target"].mean())
        scikit_model.fit(scikit_data["data"], target)

        self.scikit_model_node_count = sum(map(lambda e: e.tree_.node_count,
                                                scikit_model.estimators_))
        
        # Save the data and the model
        self.scikit_data = scikit_data
        self.scikit_model = scikit_model

        Set up the unit test by loading the dataset and training a model.
        
coremltools/test/sklearn_tests/test_random_forest_classifier.py