How does model stacking influence bias and variance in production systems?

Stacking reduces bias by combining heterogeneous models and can reduce variance if properly regularized using a meta-learner.

Stacking is an advanced ensemble technique where multiple base models are trained and their outputs are used as inputs to a meta-model. This architecture allows capturing different inductive biases from base learners. The meta-learner learns how to combine predictions optimally.

Bias is reduced because stacking aggregates diverse representations. Variance may increase if the meta-model overfits, but cross-validation-based stacking reduces this risk. In production systems, stacking requires careful design to avoid leakage and ensure stable generalization.