Back to Topic 4.3 — Machine learning approaches
4.3.10Computer Science HL5 flashcards

Model selection

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Card 1 of 54.3.10
4.3.10
Question

What does model selection weigh besides accuracy?

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All 5 Flashcards — Model selection

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Card 1concept

Question

What does model selection weigh besides accuracy?

Answer

**Explainability**, speed at prediction, data needed, cost, and **which error is worse** — any of which can outrank accuracy.

Card 2concept

Question

Why measure a baseline first?

Answer

If the complicated model barely beats a majority-class guesser or a linear fit, the **complexity is not earning its place**.

Card 3concept

Question

Is a 0.3 percentage-point accuracy gap meaningful?

Answer

Usually **no** — within the variation between cross-validation folds it is noise. Decide on the criteria that genuinely differ.

Card 4example

Question

When does interpretability outrank accuracy?

Answer

When a decision must be **justified or audited** — lending, medicine, recruitment. An unexplainable model is then not deployable at any accuracy.

Card 5process

Question

How must candidate models be compared?

Answer

On the **same** training and test data, ideally the same **cross-validation folds**, with the test set opened **once** at the end.

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