Handling Imbalanced Datasets in Classification Problems

Handling Imbalanced Datasets in Classification Problems

Fraud detection, disease diagnosis, churn prediction, equipment failure classification. What these problems share is a common structural challenge: the outcome you actually care about is rare. When 99% of your data belongs to one class, a naive model can achieve 99% accuracy by simply predicting the majority class every time, while being completely useless for […]