What is ensemble pruning and why is it needed?

Ensemble pruning removes weak or redundant models to improve efficiency and sometimes accuracy.

Large ensembles may contain redundant or low-performing models that do not contribute meaningfully to predictions. Ensemble pruning selects a subset of models that maximize accuracy while minimizing complexity. It improves inference speed, reduces memory usage, and can even improve generalization by removing noisy models.