2.4
Ensemble
Summary
- This chapter builds a structured understanding of Ensemble: core concepts, representative methods, and practical assumptions.
- You will compare model assumptions, hyperparameters, and evaluation criteria across methods.
- Implementation examples are used to connect theory with real analytical workflows.
Intuition #
Ensemble is not only about memorizing algorithms. The key is learning when each method works, which failure modes to watch, and how to read evaluation signals before tuning.
Detailed Explanation #
What You Will Learn #
- Differences in objectives, assumptions, and outputs of major methods
- How to interpret metrics and error patterns
- Parameter choices that most influence performance in practice
What You Will Be Able To Do #
- Select methods that match the task and data conditions
- Explain improvement priorities based on evaluation evidence
- Run reproducible implementation and validation loops
Recommended Learning Flow #
- Review concepts and assumptions first
- Link equations to implementation behavior
- Iterate with metric-driven evaluation