Cross-validation

Cross-Validation is a model evaluation technique in predictive modeling that systematically partitions data into complementary subsets, using one portion for training and the remaining for validation, to assess generalization performance while minimizing overfitting.

9.7K

Publications

668.2K

Citations

38.2K

Authors

7.5K

Institutions

Publications per year

2017–2026

5K

Authors

38.2K

  1. ZY
    Zhu‐Hong You

    Xinjiang Technical Institute of Physics & Chemistry, China

    52 publications · 3.3K citations

    30

    h-index

  2. YZ
    Yudong Zhang

    University of Leicester, United Kingdom

    27 publications · 2.3K citations

    25

    h-index

  3. HL
    Hao Lin

    University of Electronic Science and Technology of China, China

    25 publications · 2.9K citations

    24

    h-index

  4. KC
    Kuo‐Chen Chou

    The Gordon Life Science Institute, United States

    23 publications · 3.8K citations

    22

    h-index

  5. GW
    Grace Wahba

    University of Wisconsin–Madison, United States

    23 publications · 7.7K citations

    21

    h-index

1–5 of 38.2K

Venues

  1. Bioinformatics

    145 publications · 24K citations

    75

    h-index

  2. Scientific Reports

    234 publications · 12.2K citations

    61

    h-index

  3. PLoS ONE

    203 publications · 13.4K citations

    58

    h-index

  4. BMC Bioinformatics

    148 publications · 12.2K citations

    55

    h-index

  5. IEEE Access

    152 publications · 8.5K citations

    53

    h-index

1–5