How to compute False Accept and False reject rates using one class SVM Python

Question

How to compute False Accept and False reject rates using one class SVM? I have a user data with around 70000 samples. I am trying to apply one class SVM here. The number of -1 values obtained are 12765 and the rest are displayed as 1. From these values obtained, how do I compute False Accept Rate ?

Answer 1

You can compute it with the help of Confusion matrix.

FAR = FPR = FP/(FP + TN)

FRR = FNR = FN/(FN + TP)

where FP: False positive
      FN: False Negative
      TN: True Negative
      TP: True Positive

You can also find the original answer here