README.md

Threshold-conversion artifacts

Question: can any multiclass classifier's threshold curve be converted to a 0.5-threshold binary rule equivalent to argmax, and can the conversion preserve maximum F1?

Verdict: the conversion is an exact reparameterization of a chosen threshold (binary), or of each class's chosen threshold (multiclass). It cannot turn one fixed 0.5 rule into argmax for C >= 3, and it cannot improve F1; it only moves the selected operating point to 0.5.

Layout

• baseline/ theory, uniform simplex, sklearn digits
• text/ DistilBERT SST-2, Twitter-RoBERTa sentiment, DistilBERT emotion
• vision/ ViT-B/16 ImageNet head on CIFAR-10 (1000 classes, OOD)
• conversion_fit/ fixed C x C linear fit to argmax labels
• tabular/ 30 tabular dataset/classifier pairs with max-F1 conversion
• manifest.json experiment ids, run ids, commits, metrics, links
• report.md full synthesis (figures embedded)
• logbook.ipynb executable notebook with formulae, tables and every link

Headline numbers

model	C	n	argmax	delta_0.5	coverage	exact adaptive
sklearn digits	10	540	97.22%	0.74%	99.26%	yes
DistilBERT SST-2	2	872	91.06%	0.00%	100.00%	yes
Twitter-RoBERTa sentiment	3	2000	71.50%	4.65%	95.35%	yes
DistilBERT emotion	6	2000	92.70%	0.20%	99.80%	yes
ViT-B/16 on CIFAR-10	1000	1000	n/a	51.00%	49.00%	yes

Tabular max-F1 test: all 30 conversions are exact; F1(test, tau*) equals F1(test, converted 0.5) exactly. The selected threshold is not 0.5: binary tau* ranges 0.482-0.998, and multiclass per-class tau* spans up to 1.0. A 20-class random forest has delta_0.5 = 99.80% (998/1000 abstain), macro-F1 0.004 at 0.5, argmax F1 0.5495, and converted-argmax agreement only 87.2%.

Links

• Upstream repo: https://github.com/isllabres/python-multiagent-system
• Fork: https://github.com/jomasego/python-multiagent-system
• Bucket: https://huggingface.co/buckets/jomasego/repro-threshold-conversion-artifacts
• Models: https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest , https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion , https://huggingface.co/google/vit-base-patch16-224 , https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english
• Datasets: https://huggingface.co/datasets/cardiffnlp/tweet_eval , https://huggingface.co/datasets/dair-ai/emotion , https://huggingface.co/datasets/uoft-cs/cifar10 , https://huggingface.co/datasets/stanfordnlp/sst2

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