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[英]Why F1-score, Recall, Precision all equal to 1? (image classification linearSVM)
[英]How to return average score for precision, recall and F1-score from Sklearn Classification report?
我使用Sklearn计算精度,召回率和F1得分,得到如下结果:
precision recall f1-score support
0 0.82 0.87 0.84 2517
1 0.86 0.81 0.83 2483
avg / total 0.84 0.84 0.84 5000
我试过这段代码:
print("precision_score: ",precision_score(test_y, predicted))
print("recall_score: ",recall_score(test_y, predicted))
print("f1_score: ",f1_score(test_y, predicted))
它显示标签1的p,r和f1。
precision_score: 0.857692307692
recall_score: 0.808296415626
f1_score: 0.832262077545
但是我怎样才能返回avg / total的值呢?
它记录在classification_report页面中 :
报告的平均值是不同类别的流行加权宏观平均值(相当于precision_recall_fscore_support ,平均值='加权')。
所以要获得平均分,你可以做到:
precision, recall, f1, _ = precision_recall_fscore_support(test_y, predicted,
average='weighted')
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