What are the Best Practices in Handling Data Imbalance?
To effectively handle data imbalance, epidemiologists should follow several best practices:
1. Understand the Data: - Perform exploratory data analysis to understand the extent and nature of the imbalance. - Use domain knowledge to interpret the implications of the imbalance on the study.
2. Choose Appropriate Metrics: - Select metrics that provide a more balanced view of model performance, such as F1-score, Precision, Recall, and AUC-ROC.
3. Use Resampling Techniques: - Apply oversampling or undersampling techniques judiciously to create a more balanced dataset for model training.
4. Model Validation: - Use cross-validation techniques to ensure that the model generalizes well to unseen data.
5. Communicate Findings Clearly: - Clearly report the extent of data imbalance and the methods used to address it in study reports and publications.
