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

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

02926bce47bf12e2f64a8a38e7820524b48ab07b
Free2AITools Nexus Index
72.6
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 92
P: Popularity 72
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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Semantic Scholar 798 Citations
Paper Information Summary
Entity Passport
Registry ID 02926bce47bf12e2f64a8a38e7820524b48ab07b
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

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

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 92
Popularity (P) 72
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:72), 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{Probabilistic,
  title={Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models},
  note={Indexed by Free2AITools}
}

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📊 Research Signals

📈798CitationsSemantic Scholar
🏛️92AuthorityFNI pillar
⏱️100RecencyFNI pillar
✅65QualityFNI pillar
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semantic_scholar
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ArXiv
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7

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