Monotonic Models

Monotonic Models is a class of machine learning models in which the predicted output exhibits a guaranteed non-decreasing or non-increasing relationship with respect to specified input features, ensuring that model decisions adhere to predefined monotonicity constraints for enhanced interpretability and trustworthiness in explainable machine learning.

330

Publications

20.6K

Citations

768

Authors

430

Institutions

Publications per year

2017–2026

80

Authors

768

  1. KR
    Kevin Robert Canini

    University of California, Berkeley, United States

    5 publications · 276 citations

    5

    h-index

  2. JP
    Jan Pfeifer

    Google (United States), United States

    5 publications · 276 citations

    5

    h-index

  3. MR
    Maya R. Gupta

    University of Washington, United States

    5 publications · 276 citations

    5

    h-index

  4. TZ
    Thomas Zeugmann

    Kyushu University, Japan

    5 publications · 195 citations

    5

    h-index

  5. SL
    Steffen Lange

    Humboldt-Universität zu Berlin, Germany

    5 publications · 195 citations

    5

    h-index

1–5 of 768

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 223 citations

    8

    h-index

  2. 7

    h-index

  3. Construction and Building Materials

    6 publications · 259 citations

    6

    h-index

  4. SSRN Electronic Journal

    Repository · 5 publications · 136 citations

    5

    h-index

  5. Technometrics

    5 publications · 653 citations

    5

    h-index

1–5