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All 5 Flashcards — Model selection
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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.
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**.
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.
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.
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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Topic 4.3 hub
Machine learning approaches
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Computer Science exam skills
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