In the bias-variance decomposition of expected prediction error, what does the irreducible error term represent, and why can no model eliminate it?
Correct! Well done.
Incorrect.
The correct answer is B) The variance inherent in the data-generating process itself (label noise or unmeasured factors), which sets a lower bound on achievable error regardless of model choice or amount of data
Correct Answer
The variance inherent in the data-generating process itself (label noise or unmeasured factors), which sets a lower bound on achievable error regardless of model choice or amount of data
Expected squared error decomposes into bias² + variance + irreducible error (often written as σ², the noise variance in the true relationship between inputs and outputs). Because this noise is intrinsic to the data-generating process, it represents a theoretical floor on performance that no amount of additional data, model capacity, or tuning can remove.