H13-311_V3.5 · Question #126
The training error will continue to decrease as the model complexity increases.
The correct answer is A. TRUE. A (TRUE) is correct because training error measures how well a model fits the data it was trained on, and a more complex model always has at least as much capacity to fit that data as a simpler one - in the limit, a sufficiently complex model can memorize every training…
Question
The training error will continue to decrease as the model complexity increases.
Options
- ATRUE
- BFALSE
How the community answered
(28 responses)- A79% (22)
- B21% (6)
Explanation
A (TRUE) is correct because training error measures how well a model fits the data it was trained on, and a more complex model always has at least as much capacity to fit that data as a simpler one - in the limit, a sufficiently complex model can memorize every training example, driving training error to zero. This is the core mechanism behind overfitting: the model learns the noise and idiosyncrasies of the training set rather than the underlying pattern.
B (FALSE) is wrong because it conflates training error with generalization (test) error. While test error does eventually rise with excess complexity (the classic U-shaped bias-variance curve), training error is monotonically non-increasing as complexity grows - it never goes back up.
Memory tip: Think of a student who memorizes the answer key. The more study time (complexity) they get, the closer their score on that exact test (training error) gets to 100% - but they may still fail a new exam (test error). Training error = score on the answer key you already have.
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