1. Binary classification index

TP： Forecast as Real sample in Indeed Really quantity
FP： Forecast as Real sample in Indeed Number of fake
FN： Forecast as Fake samples in Indeed Really quantity
TN： Forecast as Fake samples in Indeed Number of fake

Accuracy rate Accuracy：

$\frac{justindeedpremeasuringOfsampleBen=justindeedpremeasuringby1+justindeedpremeasuringby0Of}{totalsampleBenCount}$ = $\frac{TP+TN}{ALL}$

Precision rate Precision：

$\frac{theYespremeasuringbyreallyOfsampleBeninreallyOfCountThe\; amount}{premeasuringbyreallysampleBenOfCountThe\; amount}$ = $\frac{TP}{TP+FP}$

Recall rate Recall：

$\frac{theYespremeasuringbyreallyOfsampleBeninreallyOfCountThe\; amount}{theYesreallysampleBenCountThe\; amount}$ = $\frac{TP}{TP+FN}$

give an example ： Predict the number of diseases .
Suppose there are 100 Samples , among 20 A sick man . Through prediction, it is found that 24 A sick man , Among them is 18 One is really sick .
In this case ：
TP = 18
FP = 24-18=6
FN= 20-18 =2
TN= （100-24）-2 = 74

Accuracy rate = $\frac{18+74}{100}=0.82$
Precision rate =$\frac{18}{18+6}$
Recall rate =$\frac{18}{18+2}$

2. Reference resources

1. https://www.jianshu.com/p/1ca37d729d0c