Nested Cross-validation

Nested Cross-Validation is a resampling procedure that employs two hierarchical layers of cross-validation—one for hyperparameter tuning and the other for model evaluation—thereby providing an almost unbiased estimate of a model’s generalization performance while mitigating overfitting in model selection.

176

Publications

12.7K

Citations

1.1K

Authors

521

Institutions

Publications per year

2017–2026

142

Authors

1.1K

  1. MF
    Markus Fischer

    Universität Hamburg, Germany

    4 publications · 128 citations

    4

    h-index

  2. NK
    Nikolaos Koutsouleris

    Ludwig-Maximilians-Universität München, Germany

    3 publications · 254 citations

    3

    h-index

  3. AR
    Anita Riecher‐Rössler

    University of Basel, Switzerland

    3 publications · 281 citations

    3

    h-index

  4. DS
    Daniel Stamate

    Goldsmiths University of London, United Kingdom

    3 publications · 162 citations

    3

    h-index

  5. KR
    Kevin R. Ward

    Virginia Commonwealth University, United States

    2 publications · 45 citations

    2

    h-index

1–5 of 1.1K

Venues

  1. Scientific Reports

    10 publications · 357 citations

    10

    h-index

  2. PLoS ONE

    7 publications · 2.1K citations

    7

    h-index

  3. Frontiers in Oncology

    6 publications · 176 citations

    6

    h-index

  4. Schizophrenia Bulletin

    4 publications · 352 citations

    4

    h-index

  5. Journal of Neural Engineering

    3 publications · 46 citations

    3

    h-index

1–5