Underfitting

**Underfitting** is a model misspecification phenomenon in machine learning in which a hypothesis of insufficient complexity fails to capture the underlying structure of the training data, thereby exhibiting high bias and poor predictive performance on both training and unseen data.

1.2K

Publications

197.9K

Citations

3.6K

Authors

1.1K

Institutions

Publications per year

2017–2026

850

Authors

3.6K

  1. MS
    Masashi Sugiyama

    Tokyo Institute of Technology, Japan

    10 publications · 544 citations

    10

    h-index

  2. GC
    Gustavo Carneiro

    The University of Adelaide, Australia

    8 publications · 950 citations

    8

    h-index

  3. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    8 publications · 1.9K citations

    8

    h-index

  4. GN
    Gang Niu

    Xi'an Jiaotong University, China

    8 publications · 470 citations

    8

    h-index

  5. YB
    Yoshua Bengio

    Université de Montréal, Canada

    8 publications · 359 citations

    8

    h-index

1–5 of 3.6K

Venues

  1. arXiv (Cornell University)

    Repository · 260 publications · 39.2K citations

    71

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 27 publications · 1.6K citations

    21

    h-index

  3. IEEE Access

    22 publications · 2.1K citations

    17

    h-index

  4. Neural Information Processing Systems

    Conference · 12 publications · 1.1K citations

    12

    h-index

  5. 12

    h-index

1–5