{"url":"/dataset/wine","name":"Wine","full_name":"Wine Data Set","description_markdown":"These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.\n\nSource: [UCI Machine Learning Repository Wine Dataset](https://archive.ics.uci.edu/ml/datasets/Wine)\nImage Source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)","description_withheld":null,"homepage":"https://archive.ics.uci.edu/ml/datasets/Wine","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/endgame-analysis-of-dou-shou-qi","title":"Endgame Analysis of Dou Shou Qi","first_author":"Jan N. van Rijn","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Graph Classification","url":"/task/graph-classification","datasets_with_task":"/datasets/task/graph-classification"},{"name":"AutoML","url":"/task/automl","datasets_with_task":"/datasets/task/automl"},{"name":"General Classification","url":"/task/classification","datasets_with_task":"/datasets/task/classification"},{"name":"Image/Document Clustering","url":"/task/imagedocument-clustering","datasets_with_task":"/datasets/task/imagedocument-clustering"},{"name":"Feature Importance","url":"/task/feature-importance","datasets_with_task":"/datasets/task/feature-importance"},{"name":"Incremental Constrained Clustering","url":"/task/incremental-constrained-clustering","datasets_with_task":"/datasets/task/incremental-constrained-clustering"}],"languages":[],"variants":["Wine"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/incremental-constrained-clustering-on-wine","task":"Incremental Constrained Clustering","dataset_variant":"Wine","rows":8,"metrics":["AUBC-ARI (quality)","AUBC-ARI (similarity)"],"first_row_in_archive_order":{"model":"MPCK-Means+NPU","paper":"/paper/incremental-constrained-clustering-by-minimal","metrics":{"AUBC-ARI (quality)":"0.893±0.016","AUBC-ARI (similarity)":"0.817±0.002"},"code_links":[{"title":"aymericb213/IAC","url":"https://github.com/aymericb213/IAC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/feature-importance-on-wine","task":"Feature Importance","dataset_variant":"Wine","rows":2,"metrics":["Pearson Correlation"],"first_row_in_archive_order":{"model":"Garson Variable Importance","paper":"/paper/variance-based-feature-importance-in-neural","metrics":{"Pearson Correlation":"0.74"},"code_links":[{"title":"rebelosa/feature-importance-neural-networks","url":"https://github.com/rebelosa/feature-importance-neural-networks"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/automl-on-wine","task":"AutoML","dataset_variant":"Wine","rows":1,"metrics":["accuracy"],"first_row_in_archive_order":{"model":"Logistic Regression","paper":"/paper/optimindtune-a-multi-agent-framework-for","metrics":{"accuracy":"98.33"},"code_links":[{"title":"MeherBhaskar/OptiMindTune","url":"https://github.com/MeherBhaskar/OptiMindTune"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/classification-on-wine","task":"General Classification","dataset_variant":"Wine","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MONT3","paper":"/paper/multi-objective-optimisation-of-multi-output","metrics":{"Accuracy":"100"},"code_links":[{"title":"vojha-code/multi-output-neural-tree","url":"https://github.com/vojha-code/multi-output-neural-tree"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/graph-classification-on-wine","task":"Graph Classification","dataset_variant":"Wine","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"sKNN-LDS","paper":"/paper/neighborhood-enlargement-in-graph-neural","metrics":{"Accuracy":"98"},"code_links":[{"title":"CODE-SUBMIT/Graph_Neighborhood_1","url":"https://github.com/CODE-SUBMIT/Graph_Neighborhood_1"},{"title":"CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network","url":"https://github.com/CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-document-clustering-on-wine","task":"Image/Document Clustering","dataset_variant":"Wine","rows":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"ELSC","paper":"/paper/ensemble-learning-for-spectral-clustering","metrics":{"Accuracy (%)":"75.8"},"code_links":[{"title":"Li-Hongmin/MyPaperWithCode","url":"https://github.com/Li-Hongmin/MyPaperWithCode"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/optimindtune-a-multi-agent-framework-for","title":"OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization","date":"2025-05-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/incremental-constrained-clustering-by-minimal","title":"Incremental Constrained Clustering by Minimal Weighted Modification","date":"2023-09-22","rows_on_this_dataset":8,"code_links":1,"syntology":null},{"paper":"/paper/ensemble-learning-for-spectral-clustering","title":"Ensemble Learning for Spectral Clustering","date":"2020-11-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-objective-optimisation-of-multi-output","title":"Multi-Objective Optimisation of Multi-Output Neural Trees","date":"2020-10-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/variance-based-feature-importance-in-neural","title":"Variance-Based Feature Importance in Neural Networks","date":"2019-10-16","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/neighborhood-enlargement-in-graph-neural","title":"Mutual Information Maximization in Graph Neural Networks","date":"2019-05-21","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}