Handling Class Imbalance & Special Modeling Scenarios Questions

Techniques for building and evaluating machine learning models when confronted with imbalanced datasets and other specialized modeling scenarios. Includes data-level methods (oversampling, undersampling, SMOTE and variants), algorithmic approaches (class weights, focal loss, cost-sensitive learning), evaluation strategies and metrics suited for imbalanced problems (precision-recall AUC, F1, balanced accuracy), threshold tuning, calibration, and robust validation (stratified cross-validation). Also covers anomaly/rare-event detection, multi-class and multi-label considerations, and practical production considerations such as model monitoring, fairness implications, and deployment trade-offs in skewed data settings.