class_docstr: Unit test class for testing scikit-learn converter.
docstr: 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.
      """
      import numpy as np
      from sklearn.datasets import load_boston
      from sklearn.ensemble import RandomForestClassifier
  
      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
      )
      t = scikit_data.target
      target = np.digitize(t, np.histogram(t)[1]) - 1
      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.target = target
      self.scikit_model = scikit_model
  
file_path: coremltools/coremltools/test/sklearn_tests/test_random_forest_classifier.py
imports: import unittest
  from coremltools._deps import _HAS_SKLEARN
label_desc: Use image node_id 1 for calling the RandomForestMultiClassClassifierScikitTest obj's underlying member method code with example usage: obj.setUpClass() without return types