Error Rate Metrics

Error Rate Metrics is a type of evaluation metric in the domain of machine learning and data science that quantifies the frequency or proportion of incorrect predictions or outcomes produced by a model or system relative to the total number of predictions or operations performed.

1.3K

Publications

88.8K

Citations

3.9K

Authors

1.3K

Institutions

Publications per year

2017–2026

300

Authors

3.9K

  1. HN
    Hermann Ney

    RWTH Aachen University, Germany

    14 publications · 977 citations

    14

    h-index

  2. FJ
    F. Jelinek

    IBM Research - Thomas J. Watson Research Center, United States

    8 publications · 214 citations

    8

    h-index

  3. IS
    I. Scott MacKenzie

    York University, Canada

    8 publications · 1.2K citations

    8

    h-index

  4. RS
    Richard Schwartz

    National Institutes of Health, United States

    7 publications · 822 citations

    7

    h-index

  5. LM
    Lidia Mangu

    IBM Research - Thomas J. Watson Research Center, United States

    7 publications · 423 citations

    7

    h-index

1–5 of 3.9K

Venues

  1. arXiv (Cornell University)

    Repository · 28 publications · 1.2K citations

    17

    h-index

  2. IEEE Transactions on Communications

    16 publications · 1.1K citations

    13

    h-index

  3. Physical Review A

    PRA · 12 publications · 2.1K citations

    12

    h-index

  4. ArXiv.org

    Repository · 10 publications · 289 citations

    10

    h-index

  5. PLoS ONE

    10 publications · 424 citations

    10

    h-index

1–5