Ensemble

A technique that combines the predictions of multiple models to produce a more accurate and robust result than any single model alone. Different models tend to make different mistakes, and aggregating their outputs reduces overall error. Ensembles are common in competition-winning machine learning pipelines and in applications where reliability matters more than speed.
See also: distillation, overfitting, baseline.