Example Concept: Class imbalance can be managed through various strategies such as resampling the dataset, using different evaluation metrics, or applying algorithmic adjustments. Resampling involves either oversampling the minority class or undersampling the majority class to balance the class distribution. Alternatively, you can use techniques like SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic examples. Algorithmic adjustments include modifying the learning algorithm to give more weight to minority classes or using ensemble methods like Random Forests that are less sensitive to class imbalance.