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):
    """Compare various learners on various datasets using cross-validation.
    Print results as a table."""
    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')
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):
    """Compare various learners on various datasets using cross-validation.
    Print results as a table."""
    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')
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' )
Compare various learners on various datasets using cross-validation.
    Print results as a table.
Compare various learners on various datasets using cross - validation . Print results as a table .