code: def test_default_configuration_classify_sparse(self):
      for i in range(2):
          X_train, Y_train, X_test, Y_test = get_dataset(
              dataset="boston", make_sparse=True
          )
          configuration_space = (
              ExtraTreesPreprocessorRegression.get_hyperparameter_search_space()
          )
          default = configuration_space.get_default_configuration()
          preprocessor = ExtraTreesPreprocessorRegression(
              random_state=1,
              **{hp_name: default[hp_name] for hp_name in default},
          )
          preprocessor.fit(X_train, Y_train)
          X_train_trans = preprocessor.transform(X_train)
          X_test_trans = preprocessor.transform(X_test)
  
          # fit a regressor on top
          regressor = ExtraTreesRegressor(random_state=1)
          predictor = regressor.fit(X_train_trans, Y_train)
          predictions = predictor.predict(X_test_trans)
          error = sklearn.metrics.mean_squared_error(
              predictions, Y_test
          )
          self.assertAlmostEqual(error, 55.69613978965742, places=2)
  
file_path: auto-sklearn/test/test_pipeline/components/feature_preprocessing/test_extra_trees_regression.py
imports: import sklearn.metrics
  from sklearn.ensemble import ExtraTreesRegressor
  from autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_regression import ExtraTreesPreprocessorRegression
  from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing, get_dataset
label_desc: Use image node_id 3 for calling the ExtraTreesRegressionComponentTest obj's underlying member method code with example usage: obj.test_default_configuration_classify_sparse() without return types