What are ensemble methods for handling imbalanced data?

What are ensemble methods for handling imbalanced data?

Ensemble methods combine multiple models to improve performance on imbalanced datasets. Techniques like bagging and boosting are commonly used.

Can you explain how boosting works in this context?

Boosting focuses on training models sequentially, where each new model corrects errors made by the previous ones, enhancing performance on minority classes.

What are some popular ensemble algorithms for imbalanced data?

Some popular algorithms include Random Forest, AdaBoost, and Gradient Boosting. They help improve classification accuracy for minority classes.

Are there any specific metrics to evaluate these models?

Yes, metrics like F1-score, precision-recall curves, and AUC-ROC are crucial for evaluating models on imbalanced datasets.