What is multi-model averaging in ensemble learning?

Multi-model averaging combines predictions from multiple models by averaging outputs to reduce variance.

Averaging ensembles combine outputs from different models either uniformly or with learned weights. This reduces variance because errors from different models cancel each other out. Weighted averaging improves performance by giving stronger models higher influence. It is commonly used in deep learning ensembles and probabilistic models.