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# "I doubt the accuracy is calculated right?"

siamak_want
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**98**Contributor II
Hi the international RM team,

I run a neural network binary classification process which the performance is evaluated with an X-Validation operator (10-fold). I receive an astonishing result:

the accuracy is : 100%, but the precision = 97.2% and also the recall=97.2%!!!

The equation for calculating accuracy is: (TP+TN)/ (TP+TN+FP+FN)

the precision can be calculated as : TP/TP+FP; So if the accuracy =100%. then FP=0 and consequently, precision=100.

Do I misunderstand something? Please explain me. any help to eliminate my doubt would be appreciated.

Thanks.

I run a neural network binary classification process which the performance is evaluated with an X-Validation operator (10-fold). I receive an astonishing result:

the accuracy is : 100%, but the precision = 97.2% and also the recall=97.2%!!!

The equation for calculating accuracy is: (TP+TN)/ (TP+TN+FP+FN)

the precision can be calculated as : TP/TP+FP; So if the accuracy =100%. then FP=0 and consequently, precision=100.

Do I misunderstand something? Please explain me. any help to eliminate my doubt would be appreciated.

Thanks.

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## Answers

1,869Unicorn98Contributor III think I found the problem, RM delivers "Weighted Mean Precision", I think this is not the precision which I wrote the equation for, in my previous post. RM also delivers "mikro average" in parenthesis just infront of the "Weighted Mean Precision". The micro average adheres to the equation which I wrote. Would you please explain me which precision should I use? I mean weighted mean or the mikro one?

Thanks again