Which statement is correct about bias, variance, and generalization error evaluated on the same problem?

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BlackTom AI · Accepted Answer

When evaluating bias, variance, and generalization error on the same problem, it’s helpful to recall what each term represents in model behavior.
Option A suggests that higher bias leads to a higher risk of overfitting. In reality, high bias generally causes underfitting: the model is too simple to capture the underlying pattern, resulting in high training and test error rather than overfitting to noise.
Option B states that models with higher variance have a higher risk of overfitting. This aligns with the classic bias-variance tradeoff: high variance means the model is sensitive to fluctuations in the training data and can fit noise, which increases overfitting and reduces generalization ability on new data.
Option C claims that higher generalization error implies a higher risk of overfitting. Generalization error is the outcome you care about on unseen data, not a property that directly causes overfitting. A model could have high generalization error due to high bias or high variance, but the statement as written confuses outcome with causation, so it isn’t a correct causal description of overfitting risk.
Option D combines B and C. Since C is not a correct causal statement about overfitting risk, this composite option is also not accurate.
To summarize the logical relationships: high bias tends to underfit rather than overfit, high variance tends to overfit, and generalization error is the result you measure on new data rather than a cause of overfitting. Therefore, the option that correctly reflects the link between variance and overfitting, while avoiding the mischaracterizations in the other statements, is the one focused on variance as the driver of overfitting without asserting that generalization error itself causes overfitting.

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Which statement is correct about bias, variance, and generalization error evaluated on the same problem?

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