Cross-validation Stability

Cross-Validation Stability is a methodological metric that quantifies the consistency of model performance estimates across different data partitions in cross-validation procedures, distinguishing itself by assessing the robustness of predictive accuracy to variations in training and validation subsets.

25

Publications

1.9K

Citations

59

Authors

43

Institutions

Publications per year

2017–2026

5

Authors

59

  1. SV
    Sergei Vassilvitskii

    Google (United States), United States

    2 publications · 45 citations

    2

    h-index

  2. KK
    Karin Kjeldahl

    University of Copenhagen, Denmark

    1 publications · 350 citations

    1

    h-index

  3. WZ
    Wenda Zhou

    Princeton University, United States

    1 publications · 12 citations

    1

    h-index

  4. ZY
    Zakariya Yahya Algamal

    University of Mosul, Iraq

    1 publications · 46 citations

    1

    h-index

  5. MP
    Michael Pencina

    Boston University, United States

    1 publications · 103 citations

    1

    h-index

1–5 of 59

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 2 publications · 147 citations

    2

    h-index

  2. Analytical and Bioanalytical Chemistry

    2 publications · 433 citations

    2

    h-index

  3. 1

    h-index

  4. Statistical Methodology

    1 publications · 171 citations

    1

    h-index

  5. Journal of Consulting Psychology

    1 publications · 19 citations

    1

    h-index

1–5