What is multi-model disagreement analysis in ensembles?

Disagreement analysis measures how much ensemble models differ in predictions to estimate uncertainty.

Multi-model disagreement is used to quantify uncertainty in ensemble systems. If models strongly disagree on an input, it indicates ambiguous or hard-to-classify regions. Metrics include vote entropy, variance, and KL divergence among predictions. This is especially useful in active learning and safety-critical systems.