我不确定这2个之间有什么区别：
classifier = NaiveBayesClassifier.train(train_d)
d1 = (nltk.classify.accuracy(classifier,train_d[:500]))*100
d2 = (nltk.classify.accuracy(classifier,train_d[:600]))*100
d3 = (nltk.classify.accuracy(classifier,train_d[:700]))*100
d4 = (nltk.classify.accuracy(classifier,train_d[:800]))*100
d5 = (nltk.classify.accuracy(classifier,train_d[:900]))*100
d6 = (nltk.classify.accuracy(classifier,train_d[:1000]))*100
d7 = (nltk.classify.accuracy(classifier,train_d[:1100]))*100
d8 = (nltk.classify.accuracy(classifier,train_d[:1200]))*100
d9 = (nltk.classify.accuracy(classifier,train_d[:1300]))*100
d10 = (nltk.classify.accuracy(classifier,train_d[:1400]))*100
dvd_results = [d1,d2,d3,d4,d5,d6,d7,d8,d9,d10]
df1 = pd.DataFrame(list(zip(sample_sizes,dvd_results)),columns=["Sample Size","Accuracy"])
display(df1)
哪个给了我结果：
Sample Size Accuracy
0 500 99.400000
1 600 99.500000
2 700 99.285714
3 800 99.000000
4 900 99.111111
5 1000 99.100000
6 1100 99.181818
7 1200 99.250000
8 1300 99.153846
9 1400 99.071429
与我原本认为的相同：
classifier_d1 = NaiveBayesClassifier.train(train_d[:500])
classifier_d2 = NaiveBayesClassifier.train(train_d[:600])
classifier_d3 = NaiveBayesClassifier.train(train_d[:700])
classifier_d4 = NaiveBayesClassifier.train(train_d[:800])
classifier_d5 = NaiveBayesClassifier.train(train_d[:900])
classifier_d6 = NaiveBayesClassifier.train(train_d[:1000])
classifier_d7 = NaiveBayesClassifier.train(train_d[:1100])
classifier_d8 = NaiveBayesClassifier.train(train_d[:1200])
classifier_d9 = NaiveBayesClassifier.train(train_d[:1300])
classifier_d10 = NaiveBayesClassifier.train(train_d[:1400])
d1 = (nltk.classify.accuracy(classifier_d1,train_d))*100
d2 = (nltk.classify.accuracy(classifier_d2,train_d))*100
d3 = (nltk.classify.accuracy(classifier_d3,train_d))*100
d4 = (nltk.classify.accuracy(classifier_d4,train_d))*100
d5 = (nltk.classify.accuracy(classifier_d5,train_d))*100
d6 = (nltk.classify.accuracy(classifier_d6,train_d))*100
d7 = (nltk.classify.accuracy(classifier_d7,train_d))*100
d8 = (nltk.classify.accuracy(classifier_d8,train_d))*100
d9 = (nltk.classify.accuracy(classifier_d9,train_d))*100
d10 = (nltk.classify.accuracy(classifier_d10,train_d))*100
dvd_results = [d1,d2,d3,d4,d5,d6,d7,d8,d9,d10]
哪个给了我结果：
Sample Size Accuracy
0 500 50.000000
1 600 50.000000
2 700 50.000000
3 800 60.142857
4 900 88.000000
5 1000 93.500000
6 1100 93.785714
7 1200 96.428571
8 1300 97.428571
9 1400 99.071429
老实说，我看不到这两个代码块之间有什么区别，因为它们都已经由分类器训练过了，而这仅仅是在看起来好像很混乱的情况下获得了准确性。另外，如果有人可以回答我的原因，那么对于700号及以下的样本，我的准确率仅为50％！部分由于这个原因，我将假设第一个块是正确的方法，而第二个块我刚刚弄乱了分类器。 las，我不知道为什么！