An ensemble can average numeric predictions, vote among categories, weight specialized models, or analyze the spread among several plausible forecasts. Differences among members provide information about uncertainty, while agreement can improve stability when individual errors are not perfectly correlated.
Ensembles require additional compute and do not guarantee independent evidence. If every member shares the same data gap or structural bias, agreement may still be confidently wrong. Evaluation should compare ensemble gains with cost and test behavior under distribution shift and rare conditions.
Acronyms and aliases
model ensemble synonymensemble learning variant
Related terms
Frequently asked questions
How can an ensemble model estimate uncertainty?
The spread among member predictions indicates how sensitive the result is to model variation, data sampling, or alternative plausible states.
Is an ensemble model always better than one model?
No. Members can share the same errors, and the added compute may not justify a small or unverified improvement on the target task.
