AI / ML

Imbalanced datasets can skew classification models. Techniques like oversampling the minority class with SMOTE or undersampling the majority class can help. Additionally, weighted loss functions, class-specific metrics (e.g., F1-score), and using ensemble methods like balanced random forests improve performance. Remember, preprocessing data is as crucial as designing the model itself when dealing with real-world ML problems.