# randomize random.seed(i) random.shuffle(dataset) # split train = dataset[:9*len(dataset)/10] test = dataset[9*len(dataset)/10:] # build classifiers nbc = NaiveBayesClassifier(train) dtc = DecisionTreeClassifier(train) # save results results_nbc.append(nbc.accuracy(test)); results_dtc.append(dtc.accuracy(test)); # output the mean of accuray print('mean of accuracy:') print('naive bayes', np.array(results_nbc).mean()) print('decision tree', np.array(results_dtc).mean()) # 2. use test_deals.txt for classification nbc = NaiveBayesClassifier(dataset) dtc = DecisionTreeClassifier(dataset) print('naive bayes classification:') for text in test_dat: print(text, nbc.classify(text))