What is ensemble learning in imbalanced datasets?

Ensembles handle imbalanced data by combining resampling techniques with robust models.

In imbalanced datasets, ensemble methods like Balanced Random Forest or EasyEnsemble improve minority class detection. They combine under-sampling of majority class with bagging or boosting strategies. Boosting methods naturally focus on misclassified minority samples, improving recall. Proper evaluation requires metrics like F1-score or AUC instead of accuracy.