Table of Contents
- 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset
- 7 Techniques to Handle Imbalanced Data
- Learning from Imbalanced Classes
- Class imbalance, redux: argues for undersampling + bagging strategy
- Estimating the class prior and posterior from noisy positives and unlabeled data
- 예측 모형에서의 클래스 불균형(class imbalance) 문제
- Handling class imbalance in customer churn prediction: AUC good. Undersampling good. no need to undersample to match the balance. no need for advanced methods.