COMPEL Glossary / k-fold-cross-validation
K-Fold Cross-Validation
K-fold cross-validation is a model evaluation technique that provides a more reliable estimate of model performance than a single train-test split.
What this means in practice
The data is divided into K equal portions (folds). The model is trained K times, each time using a different fold as the test set and the remaining K-1 folds for training. The final performance estimate is the average across all K runs. Common values of K are 5 and 10. Cross-validation is important for enterprise AI because it reduces the risk of overestimating model performance on a lucky test split, providing confidence that the model will perform reliably in production. During the COMPEL Evaluate stage, cross-validation results are part of the evidence required for the 'Validated and Approved' quality gate.
Why it matters
A single train-test split can produce misleadingly optimistic performance estimates if the test set happens to be favorable. K-fold cross-validation provides a more reliable estimate by averaging performance across multiple splits, reducing the risk of overestimating model quality. For enterprise AI where production reliability is critical, cross-validation builds confidence that models will perform consistently across the diversity of real-world data.
How COMPEL uses it
Cross-validation results are part of the evidence required for the 'Validated and Approved' quality gate during the Evaluate stage. During Model, evaluation protocols specify the use of cross-validation for candidate model assessment. The Produce stage implements cross-validation in model development pipelines. The Evaluate stage requires cross-validation performance as documentation evidence before models can advance to production deployment.
Related Terms
Other glossary terms mentioned in this entry's definition and context.