Views and Solutions from the trenches — What is Imbalanced Learning in ML? Imbalanced Learning refers to Supervised ML modeling scenarios when the target label overwhelmingly comprises a single dominant label. This is very common in many real-world scenarios such as Credit Card Fraud, Insurance Claims Fraud, Rare Disease Diagnosis, Anomaly Detection, Manufacturing Defect Detection, Churn Prediction, and so on!
