5 Strategies for Handling Unbalanced Classes in Machine Learning

Dealing with unbalanced classes is a common challenge in machine learning that can lead to biased models and inaccurate predictions. This issue often arises when one class significantly outnumbers others, such as in datasets with more dog images than bald eagle images or in political polling where certain parties dominate. To address unbalanced classes, several strategies are recommended, including gathering more data, utilizing synthetic augmentation techniques like image augmentation and SMOTE, applying undersampling and oversampling methods, and employing reweighing techniques. Each method has its limitations, such as the time-intensive nature of data collection or the risk of increasing model variance with reweighing. Importantly, these strategies should only be applied to training data to ensure that model evaluation reflects real-world performance accurately. Understanding and applying these techniques can help improve model performance by mitigating the effects of class imbalance.