Categorical Encoding

Categorical Encoding is a feature transformation technique that converts categorical variables into numerical representations while preserving the semantic structure of the categories for use in machine learning models.

253

Publications

16.5K

Citations

596

Authors

279

Institutions

Publications per year

2017–2026

71

Authors

596

  1. WT
    W. Todd Maddox

    The University of Texas at Austin, United States

    7 publications · 722 citations

    7

    h-index

  2. FG
    F. Gregory Ashby

    University of California, Santa Barbara, United States

    7 publications · 795 citations

    7

    h-index

  3. RL
    Robert L. Goldstone

    Indiana University Bloomington, United States

    5 publications · 249 citations

    5

    h-index

  4. MF
    Michael Friendly

    York University, Canada

    4 publications · 189 citations

    4

    h-index

  5. RM
    Robert M. Nosofsky

    Indiana University Bloomington, United States

    3 publications · 660 citations

    3

    h-index

1–5 of 596

Venues

  1. Memory & Cognition

    17 publications · 762 citations

    15

    h-index

  2. 11

    h-index

  3. Psychonomic Bulletin & Review

    8 publications · 310 citations

    8

    h-index

  4. Perception & Psychophysics

    7 publications · 419 citations

    7

    h-index

  5. Cognition

    6 publications · 687 citations

    6

    h-index

1–5