Handling Class Imbalance Questions

Addressing scenarios where one class significantly outnumbers others (common in fraud, churn, disease detection). Problems: accuracy becomes misleading (95% accuracy can be trivial if 95% are negative class), model biased toward majority class. Solutions: Resampling (undersampling majority, oversampling minority, or SMOTE), adjusting class weights in loss function, choosing appropriate metrics (F1, precision-recall instead of accuracy), ensemble methods. For junior level, recognize imbalance problems, understand why accuracy fails, and know multiple approaches to handling it.