为什么有两个精确度和两个召回？

Question

我使用Python，Pandas和NLTK运行Naive Bayes分类器。

我了解一般如何计算精度和召回的内容和方法，但我不明白为什么在使用以下命令时有一对精度和一对召回

from featx import precision_recall
nb_precisions, nb_recalls = precision_recall(nb_classifier, test_feats)

training_n = int(data_n * 0.25) ### changeable
featuresets = [(first_letter(name), ethnicity) for index, (name, ethnicity, last_name) in df.iterrows()] ### changeable
train_feats, test_feats = featuresets[training_n:], featuresets[:training_n] 

nb_classifier = NaiveBayesClassifier.train(train_feats)

# Performance
print "Accuracy: " + str(accuracy(nb_classifier, test_feats))

# Precision and recall
nb_precisions, nb_recalls = precision_recall(nb_classifier, test_feats)
print "Precision +: " + str(nb_precisions[ethnic_target1])
print "Precision -: " + str(nb_precisions[ethnic_non_target])
print "Recall +: " + str(nb_recalls[ethnic_target1])
print "Recall -: " + str(nb_recalls[ethnic_non_target])

Accuracy: 0.99632
Precision +: None
Precision -: 0.99632
Recall +: 0.0
Recall -: 1.0

使用人名（特征）的第一个字母对中国人和非中国人进行分类。

                                     Gold standard
                              Chinese         non-Chinese
Predicted      Chinese            A                C
               non-Chinese        B                D

我的理解是精度= A /（A + C） 并召回= D /（B + D）