Ensemble Methods: Bagging, Boosting & Stacking
Combining several mediocre models can beat one carefully tuned model — if their mistakes differ. Bagging averages independent peers to cancel noise (variance), boosting chains correctors to chase the residual signal (bias), and stacking trains a small meta-model to learn which base model to trust where.
01.The Problem: One Model Hits a Ceiling
Your best model makes mistakes on 12% of cases.
You have tried everything:
- better features → same result
- more tuning (Topic 73: hyperparameter search) → same result
- more data → not available
So the model is done?
Not quite. You have a cheap resource you never spent: your own other models.
The linear baseline was decent on price data. The random forest was decent on categories. A neural net caught the text patterns.
None is great. But here is the strange, beautiful fact that powers half of modern ML:
A committee of mediocre models can outperform any single expert on the committee.
That committee is called an ensemble.
This topic is about the three ways to build one — bagging, boosting, stacking — and the one condition that decides whether any of them works.
Three Ensemble Architectures 🥗
Three Ensemble Architectures 🥗
Bagging averages independent peers, boosting sums dependent correctors, stacking learns how to combine — each with a different failure mode to guard.
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Failure modes, high-throughput bottlenecks, and real FAANG implementation decisions.
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