如何从 nltk 分类器获得精度和召回率？

import nltk
from nltk.corpus import movie_reviews
from nltk.tokenize import word_tokenize

documents = [(list(movie_reviews.words(fileid)), category)
             for category in movie_reviews.categories()
             for fileid in movie_reviews.fileids(category)]


all_words = []

for w in movie_reviews.words():
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:3000]

def find_features(document):
    words = set(document)
    features = {}
    for w in word_features:
        features[w] = (w in words)

    return features

featuresets = [(find_features(rev), category) for (rev, category) in documents]

training_set = featuresets[500:1500]
testing_set = featuresets[:1500]

classifier = nltk.DecisionTreeClassifier.train(training_set)

print "Classifier accuracy percent:",(nltk.classify.accuracy(classifier, testing_set))*100 , "%"

string = raw_input("Enter the string: ")
print (classifier.classify(find_features(word_tokenize(string))))

此代码将显示分类器的准确性，然后获取用户的输入。它返回用户输入的字符串的极性。

但这是我的问题：因为我可以通过使用获得准确性nltk.accuracy()，是否也能得到它的精确率和召回率？

如果您使用的是 nltk 软件包，那么您似乎可以使用recall and precision函数来自nltk.metrics.scores (请参阅文档 http://www.nltk.org/api/nltk.metrics.html).

调用后函数应该可用

from nltk.metrics.scores import (precision, recall)

然后你需要打电话给他们reference（已知标签）和test（测试集上分类器的输出）集。

像下面的代码这样的东西应该产生这些集合refsets and testsets

refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)

for i, (feats, label) in enumerate(testing_set):
    refsets[label].add(i)
    observed = classifier.classify(feats)
    testsets[observed].add(i)

然后，您可以使用类似的内容查看正面预测的精确度和召回率

print( 'Precision:', nltk.metrics.precision(refsets['pos'], testsets['pos']) )
print( 'Recall:', nltk.metrics.recall(refsets['pos'], testsets['pos']) )
# `'pos'` is for the "positive" (as opposed to "negative") label