[英]How to calculate Precision, Recall and F-score using python?
The labelTrainData.csv is used to train the classifier for predicting sentiments of Testdata.csv. labelTrainData.csv 用于训练分类器以预测 Testdata.csv 的情绪。 Finally i got BagOfCentroids.csv.
最后我得到了 BagOfCentroids.csv。
labelTrainData.csv标签训练数据.csv
id sentiment Tweet
1 0 tweet_1
2 1 tweet_2
3 0 tweet_3
Testdata.csv测试数据.csv
id Tweet
1 tweet_1
2 tweet_2
3 tweet_3
BagOfCentroids.csv BagOfCentroids.csv
id sentiment
1 0
2 1
3 1
To calculate above metrics, I am trying this,为了计算上述指标,我正在尝试这个,
print 'Sentiment precision:'
nltk.metrics.precision(BagOfCentroids['sentiment'], Testdata['sentiment'])
print 'sentiment recall:'
nltk.metrics.recall(BagOfCentroids['sentiment'], Testdata['sentiment'])
print 'sentiment F-measure:'
nltk.metrics.f_measure(BagOfCentroids['sentiment'], Testdata['sentiment'])
Is there any way to calculate Precision, Recall and F-score?有没有办法计算 Precision、Recall 和 F-score?
this article will be helpful:这篇文章会有所帮助:
TEXT CLASSIFICATION FOR SENTIMENT ANALYSIS – PRECISION AND RECALL 情感分析的文本分类——精确和召回
Detail about nltk.metrics package 有关 nltk.metrics 包的详细信息
This might be because of some import issue这可能是因为一些导入问题
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