What is negative correlation learning in ensembles?

Negative correlation learning explicitly encourages ensemble models to make different errors.

Negative correlation learning introduces a penalty term in the loss function that discourages models from producing similar outputs. By explicitly penalizing correlation between model predictions, it increases diversity while maintaining accuracy. This approach improves ensemble generalization by ensuring models specialize in different regions of the input space.