FRM Part I · FRM Exam Part I · Machine-Learning Methods
For a given prediction model, the squared bias at a point is 0.04, the variance of the model's prediction is 0.09, and the irreducible error variance is 0.16. What is the expected squared prediction error at that point?
The expected squared prediction error is 0.29. It is the sum of squared bias (0.04), prediction variance (0.09) and irreducible error variance (0.16). Omitting the irreducible noise would wrongly give 0.13, because that component cannot be removed by any model.
- A0.13
- B0.29Correct
- C0.25
- D0.09
Explanation
Expected squared prediction error equals bias squared plus variance plus irreducible error: 0.04 + 0.09 + 0.16 = 0.29. Option 0.13 omits the irreducible error. Option 0.25 omits the bias term.
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