What are ensemble methods (bagging, boosting, stacking, blending), and why do ensembles work?
Ensembles win Kaggle and quietly drive most production tabular models. What matters is explaining why combining models beats one (error decorrelation), and knowing exactly how the four techniques differ in what they fix.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Ensembles win Kaggle and quietly drive most production tabular models. What matters is explaining why combining models beats one (error decorrelation), and knowing exactly how the four techniques differ in what they fix.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
