model-evaluation

Model evaluation uses metrics like accuracy, precision, recall, F1, and ROC-AUC to measure classifier and regressor performance. Select metrics based on your task type and business goals—ROC-AUC works well for imbalanced data, while F1 balances precision and recall for classification problems.

Cross-validation splits data into multiple folds to assess model stability across different subsets. Combined with hold-out test sets and distribution shift tests, it ensures your model generalizes beyond training data and catches performance degradation in production.

Fairness checks examine whether model predictions vary unfairly across demographic groups or protected attributes. Run bias assessments during development and testing to identify disparities, then monitor them in production to catch performance drift across population segments.

Production monitoring tracks model metrics and data drift over time through dashboards and alerting systems. Combined with retraining plans, it detects when model performance degrades and triggers updates before predictions become unreliable.

Validation strategy design balances training, validation, and test splits to prevent data leakage and overfitting. Proper splitting ensures metrics reflect real-world performance and your model generalizes to unseen data in classification and regression tasks.

Versioning tracks which metric configurations, validation strategies, and fairness checks were applied to each model iteration. It enables reproducibility, audit trails, and rollback when production performance degrades or bias emerges.