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Applied Machine Learning Algorithms / Module 2

Module 2 lesson

Lasso and feature selection behaviour

Unit ID: AMLA-M02-U03 Estimated active time: 25-40 minutes

Classroom explanation

This unit belongs to Regularised Linear and Logistic Models. The practical focus is regularisation, coefficients, scaling, and first linear/logistic candidates.

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.

Lasso uses an L1 penalty that can push some coefficients to zero. This makes it tempting as a feature-selection method, but the selected features can change when the data changes.

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

If two learner activity features tell a similar story, lasso may keep one and drop the other. That choice may help simplicity, but it should not be treated as final truth.

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 treat lasso-selected features as causal drivers.

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

Name one benefit and one risk of using lasso for feature selection.

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

Lasso and feature selection behaviour is useful only when it improves the decision evidence enough to justify its extra assumptions, tuning, or complexity.