Which statement about under- and overfitting is incorrect?

Question

BlackTom AI · Accepted Answer

The prompt asks which statement about under- and overfitting is incorrect, but the provided data shows no answer options to evaluate aside from an assigned answer: 'c. Usually, we can recognise overfitting by monitoring the training error.'

Let’s reason through the concept in general. Overfitting occurs when a model learns patterns that fit the training data too closely, including noise, and as a result, its performance on unseen data deteriorates. A key diagnostic is not the training error alone, but the relationship between training error and validation/test error. Typically:
- Training error continues to decrease as the model becomes more complex or trained longer.
- Validation/test error starts to decrease up to a point, then begins to rise if the model begins to overfit.

Therefore, monitoring training error in isolation does not reliably reveal overfitting. A model could show very low training error while still underperforming on validation data, indicating overfitting. This makes the statement 'Usually, we can recognise overfitting by monitoring the training error' incorrect.

If there were other options, we would evaluate each in a similar way:
- Option(s) claiming overfitting is only about training accuracy or that validation performance is irrelevant would be incorrect.
- Option(s) suggesting underfitting can be detected solely by high training error would also be misleading, since underfitting typically shows high training and validation errors.

Given the typical understanding of overfitting diagnostics, the highlighted statement conflicts with best practices, because reliable detection relies on validation or test performance, not training error alone.

Note: The absence of the full list of answer options limits a complete, option-by-option critique. If you can provide the complete set of choices, I can break down each one in detail and map them to the correct reasoning.

Which statement about under- and overfitting is incorrect?Single choice

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