A Set of Rules for Model Validation
Abstract
ABSTRACT The validation of a data‐driven model is the process of assessing the model's ability to generalize to new, unseen data in the population of interest. This paper proposes a set of general rules for model validation. These rules are designed to help practitioners create reliable validation plans and report their results transparently. While no validation scheme is flawless, these rules can help practitioners ensure their strategy is sufficient for practical use, openly discuss any limitations of their validation strategy, and report clear, comparable performance metrics.
Fields of Study & Subjects
Statistical Methods and Bayesian Inference
Tensor decomposition and applications
Machine Learning in Healthcare
Publication details
- Journal / source
- Journal of Chemometrics
- Publisher
- Wiley
- Volume
- 40
- Issue
- 2
- Publication date
- 1 February 2026
- Language
- EN