Modeling wine preferences by data mining from physicochemical properties
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the wine quality class exercise, rounding the predictions to the nearest integer yielded a substantial reduction in test error.
“He showed that if one rounds the predictions to the nearest integer, one gets a substantial reduction in test error.”
From Generative AI for Data Science 101: Coding Without Learning to Code (2025) · cites this paper as Cortez et al. 2009 a
In the wine quality class exercise, stepwise regression did not appear to have made much of a difference in prediction error relative to a regression model with all predictor variables included.
“Our conclusion from this exercise was that stepwise regression did not appear to have made much of a difference in prediction error relative to a regression model with all predictor variables included.”
From Generative AI for Data Science 101: Coding Without Learning to Code (2025) · cites this paper as Cortez et al. 2009 a
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
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Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631 · see citing works
Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners
Mei, S., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9569 · Cited by 346 · see citing works
Privacy-preserving distributed linear regression on high-dimensional data
Gascón, A., Schoppmann, P., Balle, B., Raykova, M., Doerner, J., Zahur, S., & Evans, D. (2017) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2017-0053 · Cited by 193 · see citing works
Explainable Artificial Intelligence for Tabular Data: A Survey
Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135 · see citing works
“This example uses a data set on white wine quality [101], consisting of 11 features and an output variable representing the quality of wine on a scale from 3 to 9.”
MethodCOGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Abdul, A., von der Weth, C., Kankanhalli, M., & Lim, B. Y. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376615 · Cited by 96 · see citing works
Generative AI for Data Science 101: Coding Without Learning to Code
Bien, J., & Mukherjee, G. (2025) · Journal of Statistics and Data Science Education · doi:10.1080/26939169.2024.2432397 · Cited by 15 · see citing works
“For example, we used the well-known “wine” dataset from the UCI data bank (Cortez et al. 2009b).”
Influential
- Authors
- Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J.
- Affiliations
- Centro ALGORITMI (PT)
- Corresponding author
- Cortez, Paulo
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 47
- Issue
- 4
- Pages
- 547-553
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2103459159
- Cited by
- 1,273
- References
- 1
- Citations by year
- 2026: 70 · 2025: 94 · 2024: 105 · 2023: 111 · 2022: 108 · 2021: 140 · 2020: 118 · 2019: 89 · 2018: 87 · 2017: 68 · 2016: 60 · 2015: 51 · 2014: 64 · 2013: 31 · 2012: 38
- FWCI
- 68.74
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Decision Support Systems (publishedVersion)
- RepositóriUM (Universidade do Minho) (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Life Sciences › Agricultural and Biological Sciences › Food Science › Sensory Analysis and Statistical Methods
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- Vinho Verde, physicochemical properties, support vector machine, feature selection, regression models, targeted marketing
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.
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