Ensemble learning
Expanded definition
Bagging, boosting, and stacking are classic ensemble methods. In LLM products, ensembles show up as majority vote across samples, mixed-model routers, or combining a retriever with a generator. More models mean more cost and more failure modes to monitor. An ensemble is not automatically better than one well-evaled model. Measure the ensemble on the same golden set as the members.
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Ensemble learning FAQ
What is Ensemble learning?
Ensemble learning combines several models so the group prediction is more stable than any single model.
How is Ensemble learning used in AI systems?
Bagging, boosting, and stacking are classic ensemble methods. In LLM products, ensembles show up as majority vote across samples, mixed-model routers, or combining a retriever with a generator. More models mean more cost and more failure modes to monitor. An ensemble is not automatically better than one well-evaled model. Measure the ensemble on the same golden set as the members.
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