Several methods can be employed to mitigate the effects of class imbalance:
Resampling Techniques: This includes oversampling the minority class or undersampling the majority class to achieve a balanced dataset. Synthetic Data Generation: Methods like SMOTE (Synthetic Minority Over-sampling Technique) create synthetic samples to balance the classes. Cost-sensitive Learning: Assigning higher misclassification costs to the minority class can help models learn to treat it with more importance. Anomaly Detection: Treating the minority class as an anomaly and using specialized algorithms to detect these instances.
