This skill introduces cross-validation techniques used to assess model generalization and prevent overfitting. It focuses on different strategies, statistical robustness, and integration with HPC workflows.

• External: Familiarity with model training and evaluation
• Internal: BDA5.1.2 Evaluation Metrics (recommended)

• Define cross-validation and explain its role in evaluating model generalization.
• Compare k-fold, stratified, and leave-one-out cross-validation strategies.
• Implement cross-validation efficiently on large datasets in parallel environments.
• Use cross-validation results to guide model selection and hyperparameter tuning.
• Evaluate statistical significance and variance across validation folds.

Caution: All text is AI generated