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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A measure of how far an estimator's expected prediction is from the true target value; large bias indicates systematic error.
decomposition(Contrasted along functional versus structural lines)
The analysis of a system into parts, which is not univocal and can generate competing and complementary sets of part representations depending on the principles utilized.
variance(bias-variance decomposition)
A measure of how much an estimator's prediction changes across different training datasets; a constant predictor has zero variance.