What is model ensembling via stacking?

Stacking combines multiple models using a meta-model to improve predictions.

Stacking trains multiple base learners and then uses their predictions as inputs to a higher-level model called a meta-learner. Unlike bagging or boosting, stacking learns how to best combine different models. It leverages diversity among models to improve accuracy and robustness.