Scalable Model Evaluation

Scalable Model Evaluation is a systematic methodology within machine learning for big data that enables the efficient and effective assessment of model performance across increasingly large datasets and complex models by leveraging computational scalability and statistical rigor to maintain accuracy and feasibility.

94

Publications

5.9K

Citations

651

Authors

181

Institutions

Publications per year

2017–2026

66

Authors

651

  1. CC
    Chris Chute

    Stanford University, United States

    2 publications · 197 citations

    2

    h-index

  2. NB
    Norah Borus

    Stanford University, United States

    2 publications · 197 citations

    2

    h-index

  3. PR
    Pranav Rajpurkar

    Stanford University, United States

    2 publications · 197 citations

    2

    h-index

  4. ZF
    Zhe Feng

    Pacific Northwest National Laboratory, United States

    2 publications · 102 citations

    2

    h-index

  5. ZZ
    Zhixiao Zhang

    National Tsing Hua University, Taiwan

    2 publications · 102 citations

    2

    h-index

1–5 of 651

Venues

  1. arXiv (Cornell University)

    Repository · 21 publications · 1.8K citations

    18

    h-index

  2. Bioinformatics

    2 publications · 68 citations

    2

    h-index

  3. Surgery

    2 publications · 76 citations

    2

    h-index

  4. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 1 publications · 12 citations

    1

    h-index

  5. Chemical Engineering Journal

    1 publications · 36 citations

    1

    h-index

1–5