Cited by 10,551Explainable Artificial Intelligence (XAI)Artificial Intelligence in HealthcareData Mining Algorithms and Applications

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From local explanations to global understanding with explainable AI for trees

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Lundberg, Scott M., Erion, Gabriel, Chen, Hugh, DeGrave, Alex, Prutkin, Jordan M., Nair, Bala, Katz, Ronit, Himmelfarb, Jonathan, Bansal, Nisha, Lee, Su-In·2020·Nature Machine Intelligence

DOI 10.1038/s42256-019-0138-9PMC PMC7326367

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Abstract

Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are popular non-linear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here, we improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to i) identify high magnitude but low frequency non-linear mortality risk factors in the US population, ii) highlight distinct population sub-groups with shared risk characteristics, iii) identify non-linear interaction effects among risk factors for chronic kidney disease, and iv) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model's performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains.

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Yan, Y., Piao, S. L., Hammond, W. M., Chen, A., Hong, S., Xu, H., Munson, S. M., Myneni, R. B., & Allen, C. D. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02372-1 · Cited by 138
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Sanches-Neto, F. O., Dias-Silva, J. R., Júnior, L. H. K. Q., & Carvalho-Silva, V. H. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c04326 · Cited by 132
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Jeon, J., & Cai, H. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103975 · Cited by 127
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Başağaoğlu, H., Chakraborty, D., do Lago, C. A. F., Gutierrez, L., Şahinli, M. A., Giacomoni, M. H., Furl, C. V., Mirchi, A., Moriasi, D. N., & Şengör, S. S. (2022) · Water · doi:10.3390/w14081230 · Cited by 135
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Song, Z., Cao, S., & Yang, H. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123238 · Cited by 129
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Li, Z. (2024) · Annals of the American Association of Geographers · doi:10.1080/24694452.2024.2350982 · Cited by 143
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Slack, D., Krishna, S., Lakkaraju, H., & Singh, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00692-8 · Cited by 125
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Letzgus, S., Wagner, P., Lederer, J., Samek, W., Müller, K., & Montavon, G. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2022.3153277 · Cited by 139
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Vale, D., El-Sharif, A., & Ali, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00142-y · Cited by 124
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Shendryk, Y. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.103108 · Cited by 141
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Krzyziński, M., Spytek, M., Baniecki, H., & Biecek, P. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110234 · Cited by 146
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Ibrahim, L., Mesinovic, M., Yang, K.-W. K., & Eid, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.3040166 · Cited by 125
The trans-omics landscape of COVID-19
Wu, P., Chen, D., Ding, W., Wu, P., Hou, H., Bai, Y., Zhou, Y., Li, K., Xiang, S., Liu, P., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24482-1 · Cited by 122
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Han, L., Zhao, J., Gao, Y., & Gu, Z. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103643 · Cited by 122
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Tran, V. Q. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.127103 · Cited by 123
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Webb, E. E., Liljedahl, A., Cordeiro, J. A., Loranty, M. M., Witharana, C., & Lichstein, J. W. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01455-w · Cited by 123
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Moon, I., LoPiccolo, J., Baca, S. C., Sholl, L. M., Kehl, K. L., Hassett, M. J., Liu, D., Schrag, D., & Gusev, A. (2023) · Nature Medicine · doi:10.1038/s41591-023-02482-6 · Cited by 122
Finding and removing Clever Hans: Using explanation methods to debug and improve deep models
Anders, C. J., Weber, L., Neumann, D., Samek, W., Müller, K., & Lapuschkin, S. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.015 · Cited by 145
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Self-citation
Explaining by Removing: A Unified Framework for Model ExplanationPreprint
Covert, I., Lundberg, S., & Lee, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.14878 · Cited by 162
Self-citation
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124

Authors: Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I.
Affiliations: Microsoft (United States) (US)
University of Washington (US)
Corresponding author: Lee, Su-In
ORCID: Lundberg, Scott M.
Erion, Gabriel
Chen, Hugh
DeGrave, Alex
Prutkin, Jordan M.
Nair, Bala
Katz, Ronit
Himmelfarb, Jonathan
Bansal, Nisha
Lee, Su-In
Venue: Nature Machine Intelligence
Publisher: Springer Science and Business Media LLC
Published: 2020-01-17
Volume: 2
Issue: 1
Pages: 56-67
ISSN: 2522-5839 (ISSN-L)
DOI: 10.1038/s42256-019-0138-9
PMC: PMC7326367
OpenAlex: W2999615587
Cited by: 10,551
References: 38
Citations by year: 2026: 3607 · 2025: 2330 · 2024: 1650 · 2023: 1197 · 2022: 959 · 2021: 614 · 2020: 187
FWCI: 291.51
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Machine Intelligence (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation (DGE-1256082, DBI-1759487, DBI-1552309, DBI-1355899)
American Cancer Society (127332-RSG-15-097-01-TBG)
U.S. Department of Health & Human Services | National Institutes of Health (R35 GM 128638, R01 NIA AG 061132)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
Keywords: explainable AI, tree-based models, local explanations, mortality risk, chronic kidney disease, model monitoring, machine learning in medicine

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From local explanations to global understanding with explainable AI for trees

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