What are some advanced techniques for handling class imbalance in machine learning?

What are some advanced techniques for handling class imbalance in machine learning?

There are several advanced techniques to address class imbalance, such as SMOTE, ADASYN, and ensemble methods.

Can you explain how SMOTE works?

SMOTE, or Synthetic Minority Over-sampling Technique, generates synthetic samples for the minority class by interpolating between existing samples.

What about ensemble methods? How do they help?

Ensemble methods like Random Forest and Gradient Boosting can improve performance by combining multiple models, which helps in balancing class predictions.

Are there any specific metrics I should use to evaluate models with imbalanced data?

Yes, metrics like F1-score, precision-recall curves, and AUC-ROC are more informative than accuracy for imbalanced datasets.