{"url":"/dataset/iris-1","name":"iris","full_name":"iris","description_markdown":"The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula \"all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus\".","description_withheld":null,"homepage":"https://archive.ics.uci.edu/ml/datasets/iris","introduced_date":"2020-11-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/ensemble-learning-for-spectral-clustering","title":"Ensemble Learning for Spectral Clustering","first_author":"Hongmin Li","url":null},"license":null,"modalities":[],"tasks":[{"name":"Denoising","url":"/task/denoising","datasets_with_task":"/datasets/task/denoising"},{"name":"Clustering Algorithms Evaluation","url":"/task/clustering-algorithms-evaluation","datasets_with_task":"/datasets/task/clustering-algorithms-evaluation"},{"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":"Quantum Machine Learning","url":"/task/quantum-machine-learning","datasets_with_task":"/datasets/task/quantum-machine-learning"},{"name":"Incremental Constrained Clustering","url":"/task/incremental-constrained-clustering","datasets_with_task":"/datasets/task/incremental-constrained-clustering"},{"name":"Reinforcement Learning","url":"/task/reinforcement-learning","datasets_with_task":"/datasets/task/reinforcement-learning"}],"languages":[],"variants":["iris"],"data_loaders":[{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/iris","frameworks":["tf","jax"]}],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/incremental-constrained-clustering-on-iris","task":"Incremental Constrained Clustering","dataset_variant":"iris","rows":8,"metrics":["AUBC-ARI (quality)","AUBC-ARI (similarity)"],"first_row_in_archive_order":{"model":"IAC+NPU","paper":"/paper/incremental-constrained-clustering-by-minimal","metrics":{"AUBC-ARI (quality)":"0.941±0.007","AUBC-ARI (similarity)":"0.668±0.02"},"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-iris","task":"Feature Importance","dataset_variant":"iris","rows":2,"metrics":["Pearson Correlation"],"first_row_in_archive_order":{"model":"VarImpVIANN","paper":"/paper/variance-based-feature-importance-in-neural","metrics":{"Pearson Correlation":"0.90"},"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/general-classification-on-iris","task":"General Classification","dataset_variant":"iris","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"XBNET","paper":"/paper/xbnet-an-extremely-boosted-neural-network","metrics":{"Accuracy":"100"},"code_links":[{"title":"tusharsarkar3/XBNet","url":"https://github.com/tusharsarkar3/XBNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/clustering-algorithms-evaluation-on-iris","task":"Clustering Algorithms Evaluation","dataset_variant":"iris","rows":1,"metrics":["Purity"],"first_row_in_archive_order":{"model":"CVDD","paper":"/paper/an-internal-validity-index-based-on-density","metrics":{"Purity":"0.967"},"code_links":[{"title":"hulianyu/CVDD","url":"https://github.com/hulianyu/CVDD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/denoising-on-iris","task":"Denoising","dataset_variant":"iris","rows":1,"metrics":["Average"],"first_row_in_archive_order":{"model":"PCNN+RL+HME","paper":"/paper/improving-reinforcement-learning-for-neural","metrics":{"Average":"84.61"},"code_links":[{"title":"wjn1996/PCNN_RL_HME","url":"https://github.com/wjn1996/PCNN_RL_HME"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-document-clustering-on-iris","task":"Image/Document Clustering","dataset_variant":"iris","rows":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"ELSC","paper":"/paper/ensemble-learning-for-spectral-clustering","metrics":{"Accuracy (%)":"97.7"},"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"},{"leaderboard":"/sota/quantum-machine-learning-on-iris","task":"Quantum Machine Learning","dataset_variant":"iris","rows":1,"metrics":["Average F1"],"first_row_in_archive_order":{"model":"Best Model","paper":"/paper/machine-learning-in-the-quantum-age-quantum","metrics":{"Average F1":"1"},"code_links":[{"title":"detasar/quantum_computing_notebooks","url":"https://github.com/detasar/quantum_computing_notebooks/blob/main/SVC_VS_gridSearchQSVC.ipynb"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/reinforcement-learning-on-iris","task":"Reinforcement Learning","dataset_variant":"iris","rows":1,"metrics":["10 Images, 4*4 Stitching, Exact Accuracy"],"first_row_in_archive_order":{"model":"。","paper":"/paper/efficient-training-and-design-of-photonic","metrics":{"10 Images, 4*4 Stitching, Exact Accuracy":"分类准确度"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/machine-learning-in-the-quantum-age-quantum","title":"Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines","date":"2023-10-17","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/xbnet-an-extremely-boosted-neural-network","title":"XBNet : An Extremely Boosted Neural Network","date":"2021-06-09","rows_on_this_dataset":1,"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/improving-reinforcement-learning-for-neural","title":"RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational Searching","date":"2020-10-27","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/efficient-training-and-design-of-photonic","title":"Efficient training and design of photonic neural network through neuroevolution","date":"2019-08-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/an-internal-validity-index-based-on-density","title":"An Internal Validity Index Based on Density-Involved Distance","date":"2019-03-22","rows_on_this_dataset":1,"code_links":1,"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."}