What is ensemble stacking architecture in classification systems?

Stacking combines multiple base classifiers using a meta-model that learns how to best combine their outputs.

In stacking architecture, multiple base models (e.g., trees, neural networks, linear models) generate predictions which are then fed into a meta-learner. The meta-model learns optimal weighting and correction of base predictions. This improves generalization but requires careful cross-validation to avoid leakage.