Data preprocessing improves performance in bagging by reducing noise, in boosting by enhancing feature importance, and in stacking by providing clean inputs for diverse models.

Data preprocessing does not affect the performance of ensemble methods. All methods rely on raw, unprocessed data.

Data preprocessing increases model complexity and reduces performance in all ensemble methods.

Data preprocessing is only relevant for stacking. Bagging and boosting do not benefit from data preprocessing.