code: def compare(algorithms=[PluralityLearner, NaiveBayesLearner,
                          NearestNeighborLearner, DecisionTreeLearner],
              datasets=[iris, orings, zoo, restaurant, SyntheticRestaurant(20),
                        Majority(7, 100), Parity(7, 100), Xor(100)],
              k=10, trials=1):
      
      print_table([[a.__name__.replace('Learner','')] +
                   [cross_validation(a, d, k, trials) for d in datasets]
                   for a in algorithms],
                  header=[''] + [d.name[0:7] for d in datasets], numfmt='%.2f')
docs: Compare various learners on various datasets using cross-validation.
      Print results as a table.