Concepedia

Model Interpretability

Model Interpretability is the property of a machine learning model that enables human users to understand, trace, and rationale the model’s predictions or decisions through transparent mechanisms or post-hoc explanations.

1.7K

Publications

171.9K

Citations

6K

Authors

1.8K

Institutions

Publications per year

2017–2026

1.5K

Parents

Publications

1.7K

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Dagstuhl Research Online Publication Server · 2024 · 13.3K citations · Full text

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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin · Nature Machine Intelligence · 2019 · 9K citations · Full text

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A Unified Approach to Interpreting Model Predictions

Scott Lundberg, Su‐In Lee · arXiv (Cornell University) · 2017 · 7.6K citations · Full text

, , +17

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Marco Ribeiro, Sameer Singh, Carlos Guestrin · 2016 · 5.2K citations · Full text

, , +16

The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

Takaya Saito, Marc Rehmsmeier · PLoS ONE · 2015 · 4.7K citations · Full text

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1–5 of 1.7K

Authors

6K

  1. BK
    Been Kim

    Google (United States), United States

    14 publications · 4.3K citations

    14

    h-index

  2. RC
    Rich Caruana

    Cornell University, United States

    15 publications · 2.2K citations

    14

    h-index

  3. CR
    Cynthia Rudin

    Duke University, United States

    12 publications · 10.7K citations

    11

    h-index

  4. FD
    Finale Doshi‐Velez

    Harvard University Press, United States

    10 publications · 3.9K citations

    10

    h-index

  5. SS
    Sameer Singh

    University of California, Irvine, United States

    10 publications · 6.6K citations

    10

    h-index

1–5 of 6K

Institutions

1.8K

  1. Tsinghua University

    Beijing, China

    22 h-index

    102 publications

    5.1K citations

  2. Stanford University

    Stanford, United States

    20 h-index

    81 publications

    12.9K citations

  3. Carnegie Mellon University

    Pittsburgh, United States

    20 h-index

    51 publications

    5.7K citations

  4. Chinese Academy of Sciences

    Beijing, China

    20 h-index

    77 publications

    5.4K citations

  5. Google (United States)

    Mountain View, United States

    20 h-index

    75 publications

    9.8K citations

  6. University of California, Berkeley

    Berkeley, United States

    18 h-index

    60 publications

    13.6K citations

  7. University of Washington

    Seattle, United States

    18 h-index

    37 publications

    61.6K citations

  8. Zhejiang University

    Hangzhou, China

    17 h-index

    92 publications

    3.5K citations

1–8 of 1.8K

Venues

  1. arXiv (Cornell University)

    Repository · 270 publications · 26.2K citations

    59

    h-index

  2. IEEE Access

    33 publications · 2.9K citations

    24

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 23 publications · 1.2K citations

    20

    h-index

  4. Scientific Reports

    20 publications · 1.2K citations

    18

    h-index

  5. IEEE Transactions on Fuzzy Systems

    TFS · 14 publications · 933 citations

    13

    h-index

1–5