FRM Part I · FRM Exam Part I · Machine Learning and Prediction
In the bias-variance tradeoff, increasing the complexity of a model (for example, adding many polynomial terms) typically has which effect?
Increasing model complexity typically lowers bias but raises variance. A flexible model fits the training data more closely, yet it becomes more sensitive to noise in the sample, which hurts out-of-sample performance if complexity is excessive.
- AHigher bias and lower variance
- BLower bias and higher varianceCorrect
- CLower bias and lower variance
- DHigher bias and higher variance
Explanation
More complex models fit the training data more flexibly, lowering bias, but they become sensitive to sample noise, raising variance. Simpler models show the reverse pattern.
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