An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
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ML models are used with the ultimate intention of supporting decision making.
“ML models are used with the ultimate intention of supporting decision making [92].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [92]
Decision trees, rules, and linear models are considered easily understandable and interpretable for humans.
“These models are considered easily understandable and interpretable for humans.”
From A Survey of Methods for Explaining Black Box Models (2019) · cites this paper as [36]
Existing works either provide only a general overview of the problem or focus on particular aspects, rather than a clear classification considering problem, solution, data, and predictor simultaneously.
“Existing works tend to provide just a general overview of the problem [59] highlighting unanswered questions and problems [24]. On the other hand, other works focus on particular aspects like the impact of representation formats on comprehensibility [36], or the interpretability issues in term of advantages and disadvantages of selected predictive models [27].”
From A Survey of Methods for Explaining Black Box Models (2019) · cites this paper as [36]
In the state of the art, a small set of existing interpretable models is recognized: decision trees, rules, and linear models.
“In the state of the art a small set of existing interpretable models is recognized: decision tree , rules , linear models [27,36,84].”
From A Survey of Methods for Explaining Black Box Models (2019) · cites this paper as [36]
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- Authors
- Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B.
- Affiliations
- KU Leuven (BE)
- University of Southampton (GB)
- Corresponding author
- Baesens, Bart
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 51
- Issue
- 1
- Pages
- 141-154
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2013587512
- Cited by
- 425
- References
- 73
- Citations by year
- 2026: 16 · 2025: 34 · 2024: 46 · 2023: 34 · 2022: 34 · 2021: 42 · 2020: 38 · 2019: 47 · 2018: 31 · 2017: 16 · 2016: 14 · 2015: 21 · 2014: 16 · 2013: 14 · 2012: 14
- FWCI
- 11.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Decision Support Systems (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Lirias (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- model comprehensibility, interpretability, decision tables, decision tree, classification, representation format, end-user evaluation, reaction time (psychology), answer confidence, user preferences
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 ›
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