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Applied Machine Learning Algorithms / Module 2

Module 2 lesson

Linear models as strong baselines

Unit ID: AMLA-M02-U00 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.

A linear model is often the first serious model after the dummy baseline. It is quick to train, easy to inspect, and usually strong enough to expose whether the feature set has any useful signal. Treat it as a disciplined starting point, not as a weak 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

For the learner-support dataset, a linear or logistic model asks whether progress, practice time, quiz score, and inactivity combine in a steady direction. If that simple pattern is already useful, a more complex model must explain why it is worth the extra cost.

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 skip linear models because they sound basic. A skipped baseline makes later model comparisons weaker.

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 a regularised logistic model belongs in the first candidate list.

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

Linear models as strong baselines is useful only when it improves the decision evidence enough to justify its extra assumptions, tuning, or complexity.