pith:2RHB4GDL
Measures of predictive accuracy, miscalibration and discrimination
ABC, ABC² and Gini scores rely on predictor-dependent weights that break alignment with mean-consistent loss functions and can produce dishonest model evaluations for point predictions.
arxiv:2605.12679 v1 · 2026-05-12 · stat.ME
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Claims
ABC, ABC² and Gini scores may lead to dishonest evaluation of point predictions when used for model selection; this gives support to use mean-consistent loss functions as well as the miscalibration and the discrimination measure from the Murphy's decomposition of the expected loss for model evaluation.
That the class of mean-consistent scoring functions is the appropriate reference class for honest evaluation, and that predictor-dependent weights in ABC necessarily violate alignment with this class for all relevant Bregman divergences.
ABC, ABC squared, and Gini scores for predictive accuracy rely on predictor-dependent weights that misalign with mean-consistent loss functions, supporting instead the miscalibration and discrimination components from Murphy's decomposition.
References
Receipt and verification
| First computed | 2026-05-18T03:09:50.021120Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d44e1e186bed2b66ecb0174b3ca2f642a5d114e323158dfd65fec8ed764d8a52
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2RHB4GDL5UVWN3FQC5FTZIXWIK \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d44e1e186bed2b66ecb0174b3ca2f642a5d114e323158dfd65fec8ed764d8a52
Canonical record JSON
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