In the bias-variance trade-off, increasing model complexity generally leads to:

Question

BlackTom AI · Accepted Answer

When evaluating the bias-variance trade-off, it's helpful to map model complexity to typical behaviors of bias and variance as complexity increases.
Option A: 'Lower variance and higher bias' would imply a simpler model or one that is underfitting. In such a case, increasing complexity would not usually yield lower bias, and this option describes the opposite of what happens with added complexity.
Option B: 'Higher variance and lower bias' aligns with the standard expectation: adding complexity tends to fit training data more closely (reducing bias) but can become sensitive to fluctuations in the training set (increasing variance).
Option C: 'Higher variance and higher bias' suggests a scenario where both bias and variance rise with complexity. In practice, complexity typically reduces bias (at least to a point) while increasing variance; seeing both rise together is not the typical outcome of simply increasing model complexity.
Option D: 'Lower variance and lower bias' would indicate improved generalization without trade-offs, which is generally not achieved by merely increasing complexity; this would require other techniques or model characteristics that counteract the usual variance increase.
In summary, conceptual reasoning about the bias-variance trade-off supports the view that boosting model complexity often lowers bias but raises variance, making option B the one that reflects that classic tension. The other options describe scenarios that either contradict how bias or variance typically respond to added complexity or imply simultaneous reductions that are not characteristic of standard complexity increases.

In the bias-variance trade-off, increasing model complexity generally leads to:单项选择题

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