code: def test_multioutput(self):
      # http://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py
  
      from sklearn.multioutput import MultiOutputRegressor
      from sklearn.ensemble import RandomForestRegressor
  
      # Create a random dataset
      rng = np.random.RandomState(1)
      X = np.sort(200 * rng.rand(600, 1) - 100, axis=0)
      y = np.array(
          [np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]
      ).T
      y += 0.5 - rng.rand(*y.shape)
  
      df = pdml.ModelFrame(X, target=y)
  
      max_depth = 30
  
      rf1 = df.ensemble.RandomForestRegressor(
          max_depth=max_depth, random_state=self.random_state
      )
      reg1 = df.multioutput.MultiOutputRegressor(rf1)
  
      rf2 = RandomForestRegressor(
          max_depth=max_depth, random_state=self.random_state
      )
      reg2 = MultiOutputRegressor(rf2)
  
      df.fit(reg1)
      reg2.fit(X, y)
  
      result = df.predict(reg2)
      expected = pd.DataFrame(reg2.predict(X))
      tm.assert_frame_equal(result, expected)
  
file_path: pandas-ml/pandas_ml/skaccessors/test/test_multioutput.py
imports: import numpy
  import pandas
  import pandas_ml
  import pandas_ml.util.testing
label_desc: Use image node_id 2 for calling the TestMultiOutput obj's underlying member method code with example usage: obj.test_multioutput() without return types