为什么scikitlearn说F1得分不明确,FN大于0？

Question

我运行一个python程序,调用sklearn.metrics计算精度和F1分数的方法.这是没有预测样本时的输出:

/xxx/py2-scikit-learn/0.15.2-comp6/lib/python2.6/site-packages/sklearn/metr\
ics/metrics.py:1771: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.
  'precision', 'predicted', average, warn_for)

/xxx/py2-scikit-learn/0.15.2-comp6/lib/python2.6/site-packages/sklearn/metr\
ics/metrics.py:1771: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.
  'precision', 'predicted', average, warn_for)

当没有预测样本时,意味着TP + FP为0,所以

• 精度(定义为TP /(TP + FP))为0/0,未定义,
• 如果FN不为零,则F1得分(定义为2TP /(2TP + FP + FN))为0.

在我的情况下,sklearn.metrics也将精度返回到0.8,并记为0.因此FN不为零.

但是为什么scikilearn说F1不明确？

Scikilearn使用F1的定义是什么？

Answer 1

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/classification.py

F1 = 2*(精度*召回)/(精确+召回)

如你所知,如果预测器根本没有预测正类,则精度= TP /(TP + FP) - 精度为0.

召回= TP /(TP + FN),如果预测变量不预测正类 - TP为0 - 则召回为0.

所以现在你要划分0/0.

Answer 2

精确度,召回率,F1分数和准确度计算

- In a given image of Dogs and Cats

  * Total Dogs - 12  D = 12
  * Total Cats - 8   C = 8

- Computer program predicts

  * Dogs - 8  
    5 are actually Dogs   T.P = 5
    3 are not             F.P = 3    
  * Cats - 12
    6 are actually Cats   T.N = 6 
    6 are not             F.N = 6

- Calculation

  * Precision = T.P / (T.P + F.P) => 5 / (5 + 3)
  * Recall    = T.P / D           => 5 / 12

  * F1 = 2 * (Precision * Recall) / (Precision + Recall)
  * F1 = 0.5

  * Accuracy = T.P + T.N / P + N
  * Accuracy = 0.55

维基百科参考