2.7
Feature Selection
Summary
- This chapter builds a structured understanding of Feature Selection: 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 #
Feature Selection 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