Machine Learning: Between Accuracy and Interpretability
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Considering the wide range of machine learning methods available, there are trade-offs to be made between interpretability and accuracy.
“Considering the wide range of machine learning methods available, there are trade-offs to be made between interpretability and accuracy (Bratko, 1997; Ribeiro, Singh, & Guestrin, 2016).”
From Algorithmic Accountability and Public Reason (2018) · cites this paper as Bratko, 1997
Cited by
Sorted by citations
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766 · see citing works
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598 · see citing works
Algorithmic Accountability and Public Reason
Binns, R. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0263-5 · Cited by 457 · see citing works
“Considering the wide range of machine learning methods available, there are trade-offs to be made between interpretability and accuracy (Bratko, 1997; Ribeiro, Singh, & Guestrin, 2016).”
BackgroundInterpretability of machine learning-based prediction models in healthcare
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1379 · Cited by 541 · see citing works
“Traditionally, the focus in ML research was on global interpretability in an effort to help users understand the relationship between all possible inputs to an ML model and the space of all predictions made by the model (Bratko, 1997; Martens, Huysmans, Setiono, Vanthienen, & Baesens, 2008).”
Explainable decision forest: Transforming a decision forest into an interpretable tree
Sagi, O., & Rokach, L. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.03.013 · Cited by 189 · see citing works
Vale, D., El-Sharif, A., & Ali, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00142-y · Cited by 124 · see citing works
On to the next chapter for crop breeding: Convergence with data science
Ersoz, E. S., Martin, N. F., & Stapleton, A. E. (2020) · Crop Science · doi:10.1002/csc2.20054 · Cited by 33 · see citing works
Supersparse Linear Integer Models for Interpretable Classification
Ustun, B., Tracà, S., & Rudin, C. (2013) · arXiv · doi:10.48550/arxiv.1306.6677 · Cited by 26 · see citing works
- Authors
- Bratko, I.
- Venue
- Learning, Networks and Statistics
- Publisher
- Springer Vienna
- Published
- 1997
- Pages
- 163-177
- OpenAlex
- W382458928
- Cited by
- 67
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 8 · 2022: 9 · 2021: 6 · 2020: 12 · 2019: 3 · 2018: 6 · 2017: 2 · 2016: 1 · 2015: 4 · 2014: 1 · 2013: 2
- FWCI
- 1.68
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- interpretability, prediction accuracy, knowledge-based learning, diagnosis, ecological modeling, discrete event simulation, knowledge discovery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
