Metamorphic testing seems to try to map an output of a model to a ground truth, which I guess is great if you have a database of all the known truths in the universe.
Not exactly, metamorphic testing does not need an oracle. That’s actually the reason of its popularity in ML testing. It works by perturbing the input in a way that will produce a predictable variation of the output (or possibly no variation).
Take for example a credit scoring model: you can reasonably expect that if you increase the liquidity, the credit score should not decrease. In general it is relatively easy to come up with a set of assumptions on the effect of perturbation, which allows evaluating the robustness of a model without knowing the exact ground truth.
Take for example a credit scoring model: you can reasonably expect that if you increase the liquidity, the credit score should not decrease. In general it is relatively easy to come up with a set of assumptions on the effect of perturbation, which allows evaluating the robustness of a model without knowing the exact ground truth.