ML//Evaluation//negative control

A feature (or input) plausible enough to tempt the model, constructed so it should carry no real signal. A trap you set for your own pipeline.

A feature (or input) plausible enough to tempt the model, constructed so it should carry no real signal. A trap you set for your own pipeline.

If feature selection gives it weight anyway, you have leakage, too-small samples, or a model grabbing correlation-shaped noise.

Borrowed from experimental science: the sample that must come back negative for the assay to be trusted.

Passing it is one of the few ways a small pipeline earns trust.