UrgenT Help Detecting Performance Regressions Using Machine Learning and Hardware Counters [P]

I’m working on performance regression detection using machine learning/anomaly detection. My setup is basically: Healthy runs are used to learn normal behaviour Regression runs are used to see whether the model detects the anomaly For each counter group I only have about 10 healthy samples I’m currently using leave-one-out on the healthy data to set the detection threshold The regression samples are not used during training or threshold selection I’m confused about a few things: Do I still need a normal train/validation/test split for this type of one-class anomaly detection? With only 10 heal