Phase 2: Ensembles & Evaluation · ~120 minutes · Python

Ensemble Methods

A group of weak learners, combined correctly, becomes a strong learner. This is not a metaphor. It is a theorem.

Hiring signal: Understanding of ensemble methods internals

What you will learn

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

Introduction

Type: Build Language: Python Prerequisites: Phase 2, Lesson 10 (Bias-Variance Tradeoff) Time: ~120 minutes

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

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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