The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
iml: An R package for Interpretable Machine Learning
Open accessAbstract
Complex, non-parametric models, which are typically used in machine learning, have proven to be successful in many prediction tasks.But these models usually operate as black boxes: While they are good at predicting, they are often not interpretable.Many inherently interpretable models have been suggested, which come at the cost of losing predictive power.Another option is to apply interpretability methods to a black box model after model training.Given the velocity of research on new machine learning models, it is preferable to have model-agnostic tools which can be applied to a random forest as well as to a neural network.Tools for model-agnostic interpretability methods should improve the adoption of machine learning.
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Citations
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Cited by
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
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Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677
Self-citationOpportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
Das, A., & Rad, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11371 · Cited by 504
Multi-Objective Counterfactual Explanations
Dandl, S., Molnar, C., Binder, M., & Bischl, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58112-1_31 · Cited by 271
Self-citationCausal interpretability for machine learning-problems, methods and evaluation
Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H. (2020) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3400051.3400058 · Cited by 238
Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges
Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., Casalicchio, G., Grosse-Wentrup, M., & Bischl, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-04083-2_4 · Cited by 211
Self-citationRecommendations and future directions for supervised machine learning in psychiatry
Cearns, M., Hahn, T., & Baune, B. T. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0607-2 · Cited by 127
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
Mertes, S., Huber, T., Weitz, K., Heimerl, A., & André, E. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.825565 · Cited by 119
ViCE: visual counterfactual explanations for machine learning models
Gomez, O., Holter, S., Yuan, J., & Bertini, E. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377536 · Cited by 91
Lessons and tips for designing a machine learning study using EHR data
Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46
Do Not Trust Additive Explanations
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Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail
Sajja, S., Aggarwal, N., Mukherjee, S., Manglik, K., Dwivedi, S., & Raykar, V. (2021) · Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD) · doi:10.1145/3430984.3430995 · Cited by 22
Space use and residency patterns of largemouth bass relative to a freshwater protected area
Zolderdo, A. J., Brownscombe, J. W., Abrams, A. E. I., Suski, C. D., & Cooke, S. J. (2024) · Aquatic Sciences · doi:10.1007/s00027-023-01026-x · Cited by 9
“…package (Liaw and Wiener 2002), cross-validated model accuracy was assessed using the ‘caret’ package (Kuhn et al. 2019), predictor interaction importance was calculated with the ‘iml’ package (Molnar et al. 2018) and partial dependencies were calculated using the ‘pdp’ package (Greenwell 2017).”
MethodRelevance Metric for Counterfactuals Selection in Decision Trees
Fernández, R. R., de Diego, I. M., Aceña, V., Moguerza, J. M., & Fernández-Isabel, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-33607-3_10 · Cited by 7
Dycke, N., & Gurevych, I. (2025) · arXiv · doi:10.48550/arxiv.2508.21422 · Cited by 1
Metadata
- Authors
- Molnar, C.
- Venue
- Journal of Open Source Software
- Publisher
- The Open Journal
- Published
- 2018-06-27
- Volume
- 3
- Issue
- 26
- Pages
- 786
- ISSN
- 2475-9066 (ISSN-L)
- OpenAlex
- W2810511505
- Cited by
- 718
- References
- 4
- FWCI
- 31.68
- Type
- journal article
- Language
- en
- Open access
- diamond
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
