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Applied Machine Learning Algorithms / Module 8

Module 8 lesson

Recursive and model-based feature selection

Unit ID: AMLA-M08-U02 Estimated active time: 25-40 minutes

Classroom explanation

This unit belongs to Feature Selection, Dimensionality, and Inspection. The practical focus is feature reduction, pipeline-safe selection, PCA, permutation importance, partial dependence, and ICE limits.

Start from the workflow you already know: define the problem, protect the split, build a baseline, compare honestly, and state limits. The new algorithm detail in this unit should help you make a better choice, not distract you from that workflow.

Recursive feature selection repeatedly removes weaker features. Model-based selection uses a fitted model to choose features. Both inherit the assumptions of the scoring model.

Why this matters

Algorithm names can sound more precise than they really are. A method is useful only when its assumptions, data needs, runtime cost, and explanation limits fit the decision.

In this unit, ask:

  • What kind of evidence would make this method worth trying?
  • What data shape would make it fragile?
  • What simpler baseline must it beat?
  • What limitation should appear in the final memo?

Worked example

A linear model may select a different set of fields than a tree-based model.

Use the synthetic learner-support dataset. Compare the module's candidate idea against the dummy baseline and the transparent rule baseline. The goal is not to crown a universal winner. The goal is to decide whether this method deserves a place in the candidate portfolio.

Common mistake

Do not present the selected set as universally best.

A second common mistake is to treat a stronger-sounding algorithm as automatically better. Avoid that by writing the candidate reason before looking at any score.

Practice

Write one reason selected features may change across model families.

Add one line to your algorithm comparison report explaining how this unit changes your candidate list. Include one reason to try the method and one reason to delay or reject it.

Takeaway

Recursive and model-based feature selection is useful only when it improves the decision evidence enough to justify its extra assumptions, tuning, or complexity.