Recall Thresholds
Recall Thresholds is a formal metric in information retrieval and machine learning that specifies the minimum proportion of relevant instances a system must retrieve to be considered effective, distinguishing it from other performance thresholds by focusing exclusively on the completeness of retrieved results relative to the total available relevant set.
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1.3K
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84
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54
Institutions
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2017–2026
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Learning as a Function of Word-Frequency
John F. Hall · The American Journal of Psychology · 1954 · 196 citations
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University of Oxford
Oxford, United Kingdom
2 h-index
4 publications
98 citations
University of California, Berkeley
Berkeley, United States
2 h-index
3 publications
77 citations
California Southern University
Chandler, United States
1 h-index
2 publications
40 citations
Institute of Informatics & Telecommunications
Agia Paraskevi, Greece
1 h-index
2 publications
20 citations
The Centre for Health (New Zealand)
Tauranga, New Zealand
1 h-index
1 publications
52 citations
Union College
Schenectady, United States
1 h-index
1 publications
12 citations
NHS Grampian
Aberdeen, United Kingdom
1 h-index
12 publications
624 citations

École Normale Supérieure de Lyon
Lyon, France
1 h-index
4 publications
48 citations
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2 publications · 96 citations
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2 publications · 99 citations
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2 publications · 47 citations
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2 publications · 30 citations
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