code: def test_default_configuration_classify(self):
      for i in range(2):
          X_train, Y_train, X_test, Y_test = get_dataset(
              dataset="digits", make_sparse=False
          )
          configuration_space = (
              LibLinear_Preprocessor.get_hyperparameter_search_space()
          )
          default = configuration_space.get_default_configuration()
          preprocessor = LibLinear_Preprocessor(
              random_state=1,
              **{
                  hp_name: default[hp_name]
                  for hp_name in default
                  if default[hp_name] is not None
              },
          )
  
          with ignore_warnings(feature_preprocessing_warnings):
              preprocessor.fit(X_train, Y_train)
  
          X_train_trans = preprocessor.transform(X_train)
          X_test_trans = preprocessor.transform(X_test)
  
          # fit a classifier on top
          classifier = RidgeClassifier()
          predictor = classifier.fit(X_train_trans, Y_train)
          predictions = predictor.predict(X_test_trans)
          accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
          self.assertAlmostEqual(accuracy, 0.8548876745598057, places=2)
  
file_path: auto-sklearn/test/test_pipeline/components/feature_preprocessing/test_liblinear.py
imports: import sklearn.metrics
  from sklearn.linear_model import RidgeClassifier
  from autosklearn.pipeline.components.feature_preprocessing.liblinear_svc_preprocessor import LibLinear_Preprocessor
  from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing, get_dataset
  from test.test_pipeline.ignored_warnings import feature_preprocessing_warnings, ignore_warnings
label_desc: Use image node_id 2 for calling the LiblinearComponentTest obj's underlying member method code with example usage: obj.test_default_configuration_classify() without return types