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Glossary term
Combining several models to get better predictions than any one alone.
An ensemble combines several models so their joint prediction beats any one. Bagging, as in random forests, averages independent models; boosting trains them in sequence; stacking learns how to combine them.
Diverse models that make different errors work best together.
Ensembles are a reliable way to gain accuracy and stability.