Ensemble Methods
A group of weak learners, combined correctly, becomes a strong learner. This is not a metaphor. It is a theorem.
Prerequisites: Phase 2, Lesson 10 (Bias-Variance Tradeoff)
#Learning Objectives
- Implement AdaBoost and gradient boosting from scratch and explain how boosting sequentially reduces bias
- Build a bagging ensemble and demonstrate how averaging decorrelated models reduces variance without increasing bias
- Compare bagging, boosting, and stacking in terms of what error component each method targets
- Evaluate ensemble diversity and explain why majority voting accuracy improves with more independent weak learners
Keep going. The rest of this lesson is one step away.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.