What are Common Techniques to Address Data Imbalance?
Several techniques can be employed to manage data imbalance in epidemiological studies:
1. Resampling Methods: - Oversampling: Increasing the number of minority class instances by duplicating them or generating synthetic examples using techniques like SMOTE (Synthetic Minority Over-sampling Technique). - Undersampling: Reducing the number of majority class instances to balance the dataset.
2. Algorithmic Approaches: - Using algorithms that are inherently more robust to imbalanced data, such as decision trees or ensemble methods like Random Forest and Gradient Boosting.
3. Cost-sensitive Learning: - Assigning a higher cost to misclassifying the minority class, thereby forcing the model to pay more attention to these instances.
