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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Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286
Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.907095 · Cited by 1,861
Predictive learning via rule ensembles
Friedman, J. H., & Popescu, B. E. (2008) · The Annals of Applied Statistics · doi:10.1214/07-aoas148 · Cited by 1,315
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
Techniques for interpretable machine learning
Du, M., Liu, N., & Hu, X. (2019) · Communications of the ACM · doi:10.1145/3359786 · Cited by 1,268
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
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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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