Example Concept: One common approach to handle imbalanced classes is to use resampling techniques like oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class. Additionally, choosing evaluation metrics such as F1-score, precision-recall curves, or area under the precision-recall curve (AUC-PR) instead of accuracy can provide a better assessment of model performance. Algorithms like Random Forests and Gradient Boosting can also be tuned to handle class imbalance by adjusting class weights.