I am working on an anomaly detection use case. I studied one technique of selecting the threshold that marks 5% of validation data as anomalies. how it works in anomaly detection cases. and there is also another technique which selects the threshold that maximizes the difference between TPR and FPR.
Which technique is helpful in unsupervised learning and then comparing it with ground truth.
As we can find the ideal thresholds by plotting an RC curve with TP and FP rates. but its good technique to follow in unsupervised scenario?