modelvalidation

Model validation is the process of evaluating whether a model is appropriate for its stated purpose and decision-making context. It aims to establish the credibility, reliability, and robustness of a model's outputs given the data and assumptions used to build it. Validation looks beyond statistical fit to assess real-world applicability and risk.

Validation is distinct from verification, which checks that the model was correctly implemented. Validation asks whether the model is fit for use in its intended environment and whether the evidentiary support justifies its conclusions.

Typical activities include data quality assessment, specification of intended use, and assessment of conceptual soundness. Validation uses data partitioning, out-of-sample testing, cross-validation, backtesting for time-series, and prospective or live validation where feasible. It also includes calibration and reliability checks, robustness tests, and sensitivity analysis.

Performance metrics depend on the task: predictive accuracy (RMSE, MAE), discriminative ability (AUC/ROC), classification metrics, calibration (calibration curves), Brier score, and log loss. Uncertainty quantification, scenario analysis, and stress testing are used to judge risk under variation.

Governance and documentation are central: independent validators, audit trails, model cards, version control, and adherence to standards. Validation reports summarize assumptions, data sources, limitations, and recommended controls before deployment.

Common challenges include data quality issues, non-stationarity and model drift, overfitting, and misalignment with the decision context. Ethical and fairness considerations, interpretability, and ongoing monitoring are increasingly integrated into validation to support responsible use.