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    Under 0-1 loss, bias and variance combine multiplicativel... — Carmelics
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    Challenges→The standard bias-variance decomposition does not hold under all loss functions.

    Under 0-1 loss, bias and variance combine multiplicatively rather than additively.

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    Related propositions within the same area of thought.
    The bias-variance decomposition is derived specifically from squared loss.The standard bias-variance decomposition does not hold under all loss functions.Under a 0-1 loss function, all non-zero errors are treated equally regardless of...

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    The standard bias-variance decomposition does not hold under all loss ...81%A generalized bias-variance decomposition applicable to a variety of l...80%The bias-variance decomposition is derived specifically from squared l...79%Domingos (2000) offers a generalization of the bias-variance decomposi...78%

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    Viewed from the perspective of the bias-variance trade-off, the ability to make accurate predictions from sparse data suggests that variance is the dominant source of error but that our cognitive system often manages to keep these errors within reasonable limits (Gigerenzer & Brighton 2009). Indeed, Gigerenzer and Brighton make a stronger argument, stating that “the bias-variance dilemma shows formally why a mind can be better off with an adaptive toolbox of biased, specialized heuristics” (

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