consistency testing

**Consistency Testing** is a **model validation approach that verifies whether a model produces logically consistent predictions across related inputs** — checking that the model's outputs satisfy domain constraints, monotonicity requirements, and logical coherence. **Types of Consistency Tests** - **Monotonicity**: If feature $x$ increases and all else is equal, the prediction should increase (or decrease) monotonically if the relationship is known to be monotonic. - **Transitivity**: If A > B and B > C, the model should predict A > C. - **Symmetry**: If the relationship between A and B should be symmetric, $f(A,B) = f(B,A)$. - **Boundary**: At known boundary conditions, predictions should match known physical limits. **Why It Matters** - **Physical Plausibility**: Inconsistent predictions indicate the model has not learned the underlying physics. - **Edge Cases**: Consistency tests often catch failures at extremes of the input space. - **Trust**: Engineers won't trust a model that violates known engineering relationships, even if average accuracy is high. **Consistency Testing** is **checking the model's logic** — verifying that predictions satisfy known constraints, monotonic relationships, and domain rules.