ML4EA

Part 2 · Chapter 8

Ensemble Methods

Explains how bagging, random forests, and boosting combine models to improve predictive performance, robustness, and interpretability.

Chapter map

What this chapter develops

  • Bias and variance
  • Bagging
  • Random forests
  • Boosting
  • Out-of-bag evaluation
  • Feature importance and interpretation

Engineering practice

Application and Diamond Examples

  1. 01

    Bagging for steel plate quality control

  2. 02

    Random forest for equipment failure prediction

  3. 03

    Gradient boosting for fault detection

  4. 04

    Evaluating and interpreting a random forest

Companion materials

Portal resources

Chapter catalogFind this chapter’s indexed examples and related topics.Application Example catalogOpen 4 validated notebooks with a verified portal account.AE datasetsReview dataset sources and generation details for this chapter's Application Examples.DiscussionAsk chapter-specific questions and compare engineering interpretations with other readers.