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    Under 0-1 loss, bias and variance terms can simultaneousl... — Carmelics
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    Challenges→A generalized bias-variance decomposition applicable to a variety of loss functions, including 0-1 loss, is available.

    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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    1 reason for
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    Reasons For

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    • 1.0-1 loss is discontinuous; small bias changes can flip predictions, causing variance to spike nonlinearly without offsetting bias reduction.
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    • 2.MSE decomposition assumes squared loss structure; 0-1 loss lacks additive separability, so classical bias-variance tradeoff mechanics don't apply.
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    • 3.In high-dimensional classification, regularization can simultaneously reduce bias on some examples while increasing variance on decision boundaries.
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    Reasons Against

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    • 1.The bias-variance decomposition for 0-1 loss (via misclassification rate) remains valid; simultaneous increases suggest model misspecification, not framework failure.
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    • 2.Empirically, standard regularization paths show monotonic relationships in bias-variance contributions; joint increases reflect suboptimal hyperparameter choices.
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    • 3.If both terms increase, the test error itself increases, which contradicts observed model improvement; the claim conflates local decomposition artifacts with global behavior.
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    Related

    0-1 loss is discontinuous; small bias changes can flip predictions, causing vari...A generalized bias-variance decomposition applicable to a variety of loss functi...Empirically, standard regularization paths show monotonic relationships in bias-...If both terms increase, the test error itself increases, which contradicts obser...
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    In high-dimensional classification, regularization can simultaneously reduce bia...MSE decomposition assumes squared loss structure; 0-1 loss lacks additive separa...The bias-variance decomposition for 0-1 loss (via misclassification rate) remain...

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