Ensemble Stacking & Blending

Ensemble Stacking & Blending are meta-learning frameworks combining heterogeneous base models (LightGBM, XGBoost, CatBoost, Deep MLPs, Logistic Regression) with orthogonal inductive biases to minimize variance and push performance boundaries; 1) Stacking enforces strict K-Fold Cross-Validation: base models trained on $K-1$ folds generate out-of-fold (OOF) prediction logits on the holdout fold; these concatenated leak-free OOF predictions form the meta-feature dataset training a Level-2 Meta-Learner (typically Logistic Regression or shallow Ridge); 2) Blending splits data into train and holdout sets, training base models on train and meta-learners on holdout predictions (simpler but discards data efficiency).

Kaggle winning solutions, mission-critical financial fraud risk scoring, and combining multi-modal model ensembles.

Single models possess blind spots (trees excel on tabular features but struggle on raw text; deep nets excel on embeddings but can overfit dense tables); Stacking fuses complementary strengths, consistently beating any individual standalone predictor.

Diagram the 5-fold Stacking OOF meta-feature generation and test set prediction averaging to demonstrate zero target leakage?

Compare Stacking vs Blending in sample efficiency, overfitting risks, and implementation complexity?

Why do production Stacking architectures mandate simple linear models (Ridge/Logistic Regression) as Level-2 Meta-Learners?

Explain why ensemble diversity across heterogeneous model families (GBDT + MLP + Factorization Machines) yields higher lift than homogeneous variants?

How to distill a slow 10-model Stacking ensemble into a single compact, high-throughput student network for low-latency production serving?