Concepedia

Cross-validation Leakage

Cross-Validation Leakage is a methodological flaw in machine learning evaluation where information from the validation fold inadvertently influences the training process, thereby violating the independence assumption central to cross-validation and leading to optimistically biased performance estimates.

1

Publications

123

Citations

8

Authors

6

Institutions

Publications per year

2017–2026

1

Variants

Data Leakage In Cross-validation

Parents

Publications

1

1–1 of 1

Authors

8

  1. CT
    Carlo Tessa

    University of Florence, Italy

    1 publications · 123 citations

    1

    h-index

  2. SD
    Stefano Diciotti

    University of Florence, Italy

    1 publications · 123 citations

    1

    h-index

  3. AG
    Alba García Seco de Herrera

    HES-SO University of Applied Sciences and Arts Western Switzerland, Switzerland

    1 publications · 123 citations

    1

    h-index

  4. SW
    Selamawet Workalemahu Atnafu

    University of Bologna, Italy

    1 publications · 123 citations

    1

    h-index

  5. LC
    Luca Citi

    University of Essex, United Kingdom

    1 publications · 123 citations

    1

    h-index

1–5 of 8

Institutions

6

  1. University of Essex

    Colchester, United Kingdom

    1 h-index

    2 publications

    246 citations

  2. University of Bologna

    Bologna, Italy

    1 h-index

    4 publications

    492 citations

  3. Azienda Ospedaliera Universitaria Pisana

    Pisa, Italy

    1 h-index

    1 publications

    123 citations

  4. Marconi University

    Rome, Italy

    1 h-index

    4 publications

    492 citations

  5. Laboratori Guglielmo Marconi (Italy)

    Bologna, Italy

    1 h-index

    4 publications

    492 citations

  6. Ospedale Versilia

    Camaiore, Italy

    1 h-index

    1 publications

    123 citations

1–6 of 6

Venues

  1. Scientific Reports

    1 publications · 123 citations

    1

    h-index

1–1