希望有人能帮上忙，因为这会把我逼疯的！

from nltk.classify import NaiveBayesClassifier as nbc
from nltk import word_tokenize

tokens = {'alpha', 'beta', 'gamma', 'delta'} 

list_of_tuples = [({'alpha': 10, 'beta': 10, 'gamma': 0, 'delta': 0}, 'A'),
                  ({'alpha': 0, 'beta': 0, 'gamma': 10,  'delta': 10}, 'B')]
classifier = nbc.train(list_of_tuples)

tests = ['alpha', 'beta', 'gamma', 'delta']
for test in tests:
    features = {word: (int(word in word_tokenize(test))) for word in tokens}
    print(test, " :: ", classifier.classify(features))

这给了我a，a，b，b，正如我所期望的。但是如果我做一个小小的改变-，伽马在第一个1:

list_of_tuples = [({'alpha': 10, 'beta': 10, 'gamma': 1, 'delta': 0}, 'A'),
                  ({'alpha': 0, 'beta': 0, 'gamma': 10,  'delta': 10}, 'B')]

我仍然希望得到a，a，b，b。但是相反，我得到了b，b，b，b。有什么好处？