C7Ensembles

Ensembles combine many models into one that is more accurate and more stable than any single member. The insight is statistical: averaging diverse models cancels their independent errors, so a committee of mediocre learners can outperform a finely tuned single model. This is why nearly every winning solution on tabular data is an ensemble of some kind.

You will work out why ensembles help (bagging shrinks variance, boosting shrinks bias) and see correlated errors as the failure mode that caps the gains. Then you will train a meta-learner on out-of-fold predictions with stacking and blending, combine models cheaply with snapshot ensembles, voting, and rank averaging, and survey AutoML platforms that automate the search over pipelines and hyperparameters.

These patterns are the standard last mile of competitive and production modeling, and the diversity-and-aggregation principle here reappears in deep ensembles for uncertainty and in multi-model routing strategies for LLM systems.