Overfitting In Trees

Overfitting in Trees is a modeling phenomenon in machine learning where a decision tree learns training data patterns so intricately that it captures noise and irrelevant fluctuations, thereby compromising its generalization to unseen data.

51

Publications

9.5K

Citations

137

Authors

78

Institutions

Publications per year

2017–2026

21

Authors

137

  1. TK
    Tin Kam Ho

    Nokia (United States), United States

    2 publications · 6.9K citations

    2

    h-index

  2. MB
    Marko Bohanec

    Jožef Stefan Institute, Slovenia

    2 publications · 223 citations

    2

    h-index

  3. IB
    Ivan Bratko

    University of Ljubljana, Slovenia

    2 publications · 223 citations

    2

    h-index

  4. XZ
    Xiaolong Zhang

    Wuhan University of Science and Technology, China

    1 publications · 27 citations

    1

    h-index

  5. MC
    Marcin Czajkowski

    Bialystok University of Technology, Poland

    1 publications · 58 citations

    1

    h-index

1–5 of 137

Venues

  1. Expert Systems with Applications

    3 publications · 142 citations

    3

    h-index

  2. Machine Learning

    2 publications · 223 citations

    2

    h-index

  3. 2

    h-index

  4. IEEE Transactions on Neural Networks

    2 publications · 156 citations

    2

    h-index

  5. Open Repository and Bibliography (University of Liège)

    Repository · 1 publications · 82 citations

    1

    h-index

1–5