Training-validation Split

Training-Validation Split is a data partitioning strategy that belongs to the class of model evaluation techniques, specifically distinguished by its division of a dataset into two disjoint subsets: one for training a model and the other for validating its performance during development.

89

Publications

7.6K

Citations

408

Authors

183

Institutions

Publications per year

2017–2026

69

Authors

408

  1. VR
    V. Roshan Joseph

    Georgia Institute of Technology, United States

    3 publications · 935 citations

    3

    h-index

  2. JH
    Jetan H. Badhiwala

    University of Toronto, Canada

    2 publications · 133 citations

    2

    h-index

  3. GC
    Graeme C. Dandy

    The University of Adelaide, Australia

    2 publications · 336 citations

    2

    h-index

  4. HR
    Holger R. Maier

    The University of Adelaide, Australia

    2 publications · 336 citations

    2

    h-index

  5. MG
    Michael G. Fehlings

    University of Toronto, Canada

    2 publications · 133 citations

    2

    h-index

1–5 of 408

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 420 citations

    9

    h-index

  2. IEEE Access

    2 publications · 99 citations

    2

    h-index

  3. PLoS ONE

    2 publications · 148 citations

    2

    h-index

  4. Sensors

    2 publications · 72 citations

    2

    h-index

  5. Scientific Reports

    2 publications · 186 citations

    2

    h-index

1–5