X-Validation
A model validation technique (often abbreviated “X-Val”) that partitions data into folds to rigorously assess model generalization and detect overfitting.
Definition
A k-fold cross-validation method where the dataset is split into k subsets; the model is trained on k–1 folds and validated on the remaining fold, iterating so each subset serves as validation once. This provides robust estimates of out-of-sample performance and variance, helping governance teams set performance thresholds, detect overfitting, and decide on model readiness. Results—mean and standard deviation across folds—are documented in validation reports.
Real-World Example
A marketing-analytics team applies 10-fold X-Validation to its customer-churn model, reporting an average AUC of 0.87 ± 0.02. The low variance indicates stable generalization; these results are included in the formal validation report required by the AI Governance Office before production deployment.
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