If AUC is under 0.50, is there model tuning aside from # trees in RF that can be used?

[shlid007]

Running AutoScore on test data (fraud dataset), the AUC was under 0.50. Can model tuning address this? I didn't see suggestions for model tuning in the article (other than fine-tuning the cutoff points).

[fengx13]

Hi, thanks for the question. It depends on how low the AUC is. If it's below 0.4 or 0.3, you can just swap your binary outcome to get AUC > 0.5. But if the AUC is around 0.4-0.5, it may means the predictive value is very low