Incorrect score interpretation for the Detection Metrics

[npatki]

Currently, the docs for the detection metrics (for both single table and sequential) mention that the final score is 1 - avg(ROC AUC). The also provide an interpretation for the score, assuming it's 1 - avg(ROC AUC):

However, in the codebase, we see a slightly different formula used for computing the score. It's more accurate to say that:

score = 1 - [MAX(0.5, roc auc)*2 - 1]

Which means:

• A final score of 1 means that the original ROC AUC was in the [0, 0.5] range -- i.e. the ML was unable to tell the real data apart from the synthetic, which is good for quality but potentially bad for privacy.
• A final score of 0 means that the original ROC AUC was 1 -- i.e. the ML could fully detect the real data apart from the synthetic. this is bad for quality but potentially good for privacy.

[npatki]

This has now been updated in the page linked above. (May require a page refresh depending on your cache settings!)