Example Concept: One common approach to address class imbalance is resampling, which includes techniques like oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class. Another strategy is to use algorithmic adjustments such as cost-sensitive learning, where different misclassification costs are assigned to different classes. Additionally, ensemble methods like Random Forests or Gradient Boosting can be effective, as they inherently handle imbalance better by focusing on difficult-to-classify instances.