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    LoyalLoyalJusticeJustice
    Made withinDC&Austin
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    Home/Original/inverse
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    Inverse View

    It is not the case that A generalized bias-variance decomposition applicable to a variety of loss functions, including 0-1 loss, is available.

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    2 perspectives
    Reason for 1 of 2
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    • 1.Domingos' generalization preserves the bias-variance *labels* but loses their epistemic interpretability under 0-1 loss, making it nominally rather than substantively general.
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    • 2.Under 0-1 loss, bias and variance terms can simultaneously increase together, violating the foundational tradeoff structure the decomposition was meant to illuminate.
      ?

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    Reason for 2 of 2
    ?
    • 1.A decomposition that requires redefining 'bias' and 'variance' relative to each loss function yields family-relative, not universal, concepts—undermining the claim of genuine generalization.
      ?

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    • 2.Goodman's grue-like concerns about projectibility apply here: concepts redefined per loss function lack the entrenchment needed to support inductive inference across modeling contexts.
      ?

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    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.The standard decomposition is limited to squared loss.
      ?

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    • 2.Different inference tasks and stakes call for different loss functions.
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    • 3.Domingos (2000) offers a generalization of the bias-variance decomposition that applies to a variety of loss functions.
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