Classification Accuracy Metrics

Classification Accuracy Metrics is a statistical evaluation framework used to quantify the performance of a classification model by measuring the proportion of correctly classified instances relative to the total number of instances, distinguishing it from other evaluation methods through its focus on overall correctness in categorical predictions.

1.1K

Publications

115.5K

Citations

3.4K

Authors

1.4K

Institutions

Publications per year

2017–2026

650

Variants

Classification Accuracy Measures

Authors

3.4K

  1. PF
    Peter Flach

    University of Bristol, United Kingdom

    10 publications · 1.6K citations

    10

    h-index

  2. DJ
    David J. Hand

    Imperial College London, United Kingdom

    7 publications · 1.3K citations

    7

    h-index

  3. DC
    Davide Chicco

    University of Toronto, Canada

    6 publications · 7.8K citations

    6

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  4. GJ
    Giuseppe Jurman

    Fondazione Bruno Kessler, Italy

    6 publications · 8.2K citations

    6

    h-index

  5. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    5 publications · 317 citations

    5

    h-index

1–5 of 3.4K

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 7K citations

    24

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  2. IEEE Access

    30 publications · 2.9K citations

    22

    h-index

  3. PLoS ONE

    19 publications · 3.4K citations

    18

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  4. Scientific Reports

    11 publications · 1.3K citations

    15

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  5. Remote Sensing

    16 publications · 981 citations

    14

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1–5