Catboost Encoding

CatBoost Encoding is a categorical encoding technique that belongs to the class of target-based encoding methods, distinguished by its use of ordered target statistics computed from preceding instances in a randomly permuted dataset to mitigate target leakage and reduce overfitting.

11

Publications

679

Citations

33

Authors

19

Institutions

Publications per year

2017–2026

11

Authors

33

  1. FX
    Fengchao Xiao

    University of Electro-Communications, Japan

    1 publications · 61 citations

    1

    h-index

  2. SG
    Sebastian Gnat

    University of Szczecin, Poland

    1 publications · 29 citations

    1

    h-index

  3. SZ
    Songhua Zhang

    Hunan Institute of Technology, China

    1 publications · 28 citations

    1

    h-index

  4. CL
    Cheng Liu

    Dalian University of Technology, China

    1 publications · 61 citations

    1

    h-index

  5. NN
    Nakka Narmada

    Amrita Vishwa Vidyapeetham, India

    1 publications · 21 citations

    1

    h-index

1–5 of 33

Venues

  1. Computers & Geosciences

    CAGEO · 1 publications · 61 citations

    1

    h-index

  2. Technological Forecasting and Social Change

    1 publications · 376 citations

    1

    h-index

  3. Applied Soft Computing

    1 publications · 54 citations

    1

    h-index

  4. Signal Processing

    1 publications · 28 citations

    1

    h-index

  5. Journal of Physics Conference Series

    1 publications · 13 citations

    1

    h-index

1–5