Example Concept: Imbalanced datasets can be addressed using resampling techniques like oversampling the minority class (e.g., SMOTE - Synthetic Minority Over-sampling Technique) or undersampling the majority class. Algorithm-level approaches include adjusting class weights in models like logistic regression or decision trees to penalize misclassification of the minority class more heavily. Ensemble methods like Random Forests or Gradient Boosting can also be adapted to handle imbalance by incorporating these techniques.