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AI & Machine Learning · Intermediate

Ensemble Learning

Combining predictions from multiple models to achieve better performance than any single model.

Definition

Ensembles reduce variance, bias or both by aggregating diverse models. The main approaches are bagging (parallel models on bootstrap samples), boosting (sequential models fixing errors) and stacking (a meta-model learning how to combine base models).

Exam tip

Bagging targets variance, boosting targets bias, stacking learns a combiner.

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