A model validation team is assessing whether a gradient-boosted model used for anti-money laundering (AML) detection is stable. Which technique BEST evaluates the stability of model predictions to small changes in input data?

Sensitivity analysis and stress testing are correct because they directly test whether small, plausible changes to input values cause disproportionately large changes in model output scores. High sensitivity to small input perturbations indicates model instability, which is a reliability and auditability concern for AML models where investigators rely on score stability to prioritize cases. Out-of-time validation using data from a different time period than the training set tests temporal generalization. Cross-validation using repeated partitioning of the training data tests generalization within the training distribution. Holdout test set evaluation using a random sample withheld from model training measures overall predictive accuracy, not input sensitivity.