H13-311_V3.5 · Question #191
The test error will keep getting smaller as the complexity of the model increases.
The correct answer is B. FALSE. B is correct because increasing model complexity eventually leads to overfitting - the model memorizes training data noise rather than learning generalizable patterns, causing test error to rise after a certain point. This phenomenon is captured by the bias-variance tradeoff…
Question
The test error will keep getting smaller as the complexity of the model increases.
Options
- ATRUE
- BFALSE
How the community answered
(35 responses)- A29% (10)
- B71% (25)
Explanation
B is correct because increasing model complexity eventually leads to overfitting - the model memorizes training data noise rather than learning generalizable patterns, causing test error to rise after a certain point. This phenomenon is captured by the bias-variance tradeoff: as complexity grows, variance increases and eventually dominates, inflating test error.
Why A is wrong: The statement describes only half the story. Test error does decrease initially as complexity rises (reducing bias), but it then increases once the model begins overfitting - forming the classic U-shaped test error curve.
Memory tip: Think of the "Goldilocks zone" - a model that's too simple underfits (high bias), one that's too complex overfits (high variance), and only one in the middle generalizes well. Test error is not monotonically decreasing with complexity.
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