Holdout Evaluation
Holdout Evaluation is a model validation technique in the evaluation of recommender systems that involves partitioning the dataset into distinct training and test sets to assess the model's predictive performance on unseen data.
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3K
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194
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81
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2017–2026
28
Variants
Hold-out EvaluationHold-out MethodHoldout Method
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Avrim Blum, Adam Tauman Kalai, John Langford · 1999 · 263 citations · Full text
MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis
Ankur Gupta, Rahul Kumar, Harkirat Singh Arora et al. · IEEE Access · 2019 · 159 citations · Full text
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University of Michigan
Ann Arbor, United States
3 h-index
7 publications
288 citations

The University of Sydney
Sydney, Australia
2 h-index
4 publications
129 citations
Google (United States)
Mountain View, United States
2 h-index
7 publications
261 citations
University of Technology Sydney
Sydney, Australia
2 h-index
2 publications
78 citations
University of California, Berkeley
Berkeley, United States
2 h-index
7 publications
527 citations

University of Algarve
Faro, Portugal
1 h-index
1 publications
37 citations
De La Salle University
Manila, Philippines
1 h-index
2 publications
22 citations
University of Pennsylvania
Philadelphia, United States
1 h-index
3 publications
747 citations
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Repository · 5 publications · 197 citations
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3 publications · 219 citations
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2 publications · 193 citations
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1 publications · 154 citations
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JDM · 1 publications · 34 citations
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