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Paper

Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models

by Independent / Community 02926bce47bf12e2f64a8a38e7820524b48ab07b

Free2AITools Nexus Index

72.6

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 92

P: Popularity 71

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete picture of the model’s behavior. We present a probabilistic extension of Precision, Recall, and F1 score, which we refer to as confidence-Precision (cPrecision), confidence-Recall (cRecall), and confidence-F1 (cF1) respectively. The proposed metric...

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Paper Information Summary
Entity Passport
Registry ID	02926bce47bf12e2f64a8a38e7820524b48ab07b
License	ArXiv
Provider	semantic_scholar

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BibTeX

@misc{02926bce47bf12e2f64a8a38e7820524b48ab07b,
  author = {Unknown},
  title = {Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/02926bce47bf12e2f64a8a38e7820524b48ab07b}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models [Paper]. Free2AITools. https://api.semanticscholar.org/02926bce47bf12e2f64a8a38e7820524b48ab07b

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⚖️ Free2AITools Nexus Index V2.0

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 92

Popularity (P) 71

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models: Authority (A:92), Popularity (P:71), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete picture of the model’s behavior. We present a probabilistic extension of Precision, Recall, and F1 score, which we refer to as confidence-Precision (cPrecision), confidence-Recall (cRecall), and confidence-F1 (cF1) respectively. The proposed metric..."

❝ Cite Node

@article{Unknown2026Probabilistic,
  title={Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈754CitationsSemantic Scholar

🏛️92AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

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architecture: null
params billions: 7
context length: null
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citations: 754

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