The Iris flower data set or Fisher's Iris data set is a multivariate data set used and made famous by the British statistician 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".
The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish each species. Fisher's paper was published in the Annals of Eugenics (today the Annals of Human Genetics).
Use of the data set
Originally used as an example data set on which Fisher's linear discriminant analysis was applied, it became a typical test case for many statistical classification techniques in machine learning such as support vector machines.
The use of this data set in cluster analysis however is not common, since the data set only contains two clusters with rather obvious separation. One of the clusters contains Iris setosa, while the other cluster contains both Iris virginica and Iris versicolor and is not separable without the species information Fisher used. This makes the data set a good example to explain the difference between supervised and unsupervised techniques in data mining: Fisher's linear discriminant model can only be obtained when the object species are known: class labels and clusters are not necessarily the same.
Nevertheless, all three species of Iris are separable in the projection on the nonlinear and branching principal component. The data set is approximated by the closest tree with some penalty for the excessive number of nodes, bending and stretching. Then the so-called "metro map" is constructed. The data points are projected into the closest node. For each node the pie diagram of the projected points is prepared. The area of the pie is proportional to the number of the projected points. It is clear from the diagram (left) that the absolute majority of the samples of the different Iris species belong to the different nodes. Only a small fraction of Iris-virginica is mixed with Iris-versicolor (the mixed blue-green nodes in the diagram). Therefore, the three species of Iris (Iris setosa, Iris virginica and Iris versicolor) are separable by the unsupervising procedures of nonlinear principal component analysis. To discriminate them, it is sufficient just to select the corresponding nodes on the principal tree.
Data set
The dataset contains a set of 150 records under five attributes: sepal length, sepal width, petal length, petal width and species.
| Dataset order | Sepal length | Sepal width | Petal length | Petal width | Species |
|---|---|---|---|---|---|
| 1 | 5.1 | 3.5 | 1.4 | 0.2 | I. setosa |
| 2 | 4.9 | 3.0 | 1.4 | 0.2 | I. setosa |
| 3 | 4.7 | 3.2 | 1.3 | 0.2 | I. setosa |
| 4 | 4.6 | 3.1 | 1.5 | 0.2 | I. setosa |
| 5 | 5.0 | 3.6 | 1.4 | 0.3 | I. setosa |
| 6 | 5.4 | 3.9 | 1.7 | 0.4 | I. setosa |
| 7 | 4.6 | 3.4 | 1.4 | 0.3 | I. setosa |
| 8 | 5.0 | 3.4 | 1.5 | 0.2 | I. setosa |
| 9 | 4.4 | 2.9 | 1.4 | 0.2 | I. setosa |
| 10 | 4.9 | 3.1 | 1.5 | 0.1 | I. setosa |
| 11 | 5.4 | 3.7 | 1.5 | 0.2 | I. setosa |
| 12 | 4.8 | 3.4 | 1.6 | 0.2 | I. setosa |
| 13 | 4.8 | 3.0 | 1.4 | 0.1 | I. setosa |
| 14 | 4.3 | 3.0 | 1.1 | 0.1 | I. setosa |
| 15 | 5.8 | 4.0 | 1.2 | 0.2 | I. setosa |
| 16 | 5.7 | 4.4 | 1.5 | 0.4 | I. setosa |
| 17 | 5.4 | 3.9 | 1.3 | 0.4 | I. setosa |
| 18 | 5.1 | 3.5 | 1.4 | 0.3 | I. setosa |
| 19 | 5.7 | 3.8 | 1.7 | 0.3 | I. setosa |
| 20 | 5.1 | 3.8 | 1.5 | 0.3 | I. setosa |
| 21 | 5.4 | 3.4 | 1.7 | 0.2 | I. setosa |
| 22 | 5.1 | 3.7 | 1.5 | 0.4 | I. setosa |
| 23 | 4.6 | 3.6 | 1.0 | 0.2 | I. setosa |
| 24 | 5.1 | 3.3 | 1.7 | 0.5 | I. setosa |
| 25 | 4.8 | 3.4 | 1.9 | 0.2 | I. setosa |
| 26 | 5.0 | 3.0 | 1.6 | 0.2 | I. setosa |
| 27 | 5.0 | 3.4 | 1.6 | 0.4 | I. setosa |
| 28 | 5.2 | 3.5 | 1.5 | 0.2 | I. setosa |
| 29 | 5.2 | 3.4 | 1.4 | 0.2 | I. setosa |
| 30 | 4.7 | 3.2 | 1.6 | 0.2 | I. setosa |
| 31 | 4.8 | 3.1 | 1.6 | 0.2 | I. setosa |
| 32 | 5.4 | 3.4 | 1.5 | 0.4 | I. setosa |
| 33 | 5.2 | 4.1 | 1.5 | 0.1 | I. setosa |
| 34 | 5.5 | 4.2 | 1.4 | 0.2 | I. setosa |
| 35 | 4.9 | 3.1 | 1.5 | 0.2 | I. setosa |
| 36 | 5.0 | 3.2 | 1.2 | 0.2 | I. setosa |
| 37 | 5.5 | 3.5 | 1.3 | 0.2 | I. setosa |
| 38 | 4.9 | 3.6 | 1.4 | 0.1 | I. setosa |
| 39 | 4.4 | 3.0 | 1.3 | 0.2 | I. setosa |
| 40 | 5.1 | 3.4 | 1.5 | 0.2 | I. setosa |
| 41 | 5.0 | 3.5 | 1.3 | 0.3 | I. setosa |
| 42 | 4.5 | 2.3 | 1.3 | 0.3 | I. setosa |
| 43 | 4.4 | 3.2 | 1.3 | 0.2 | I. setosa |
| 44 | 5.0 | 3.5 | 1.6 | 0.6 | I. setosa |
| 45 | 5.1 | 3.8 | 1.9 | 0.4 | I. setosa |
| 46 | 4.8 | 3.0 | 1.4 | 0.3 | I. setosa |
| 47 | 5.1 | 3.8 | 1.6 | 0.2 | I. setosa |
| 48 | 4.6 | 3.2 | 1.4 | 0.2 | I. setosa |
| 49 | 5.3 | 3.7 | 1.5 | 0.2 | I. setosa |
| 50 | 5.0 | 3.3 | 1.4 | 0.2 | I. setosa |
| 51 | 7.0 | 3.2 | 4.7 | 1.4 | I. versicolor |
| 52 | 6.4 | 3.2 | 4.5 | 1.5 | I. versicolor |
| 53 | 6.9 | 3.1 | 4.9 | 1.5 | I. versicolor |
| 54 | 5.5 | 2.3 | 4.0 | 1.3 | I. versicolor |
| 55 | 6.5 | 2.8 | 4.6 | 1.5 | I. versicolor |
| 56 | 5.7 | 2.8 | 4.5 | 1.3 | I. versicolor |
| 57 | 6.3 | 3.3 | 4.7 | 1.6 | I. versicolor |
| 58 | 4.9 | 2.4 | 3.3 | 1.0 | I. versicolor |
| 59 | 6.6 | 2.9 | 4.6 | 1.3 | I. versicolor |
| 60 | 5.2 | 2.7 | 3.9 | 1.4 | I. versicolor |
| 61 | 5.0 | 2.0 | 3.5 | 1.0 | I. versicolor |
| 62 | 5.9 | 3.0 | 4.2 | 1.5 | I. versicolor |
| 63 | 6.0 | 2.2 | 4.0 | 1.0 | I. versicolor |
| 64 | 6.1 | 2.9 | 4.7 | 1.4 | I. versicolor |
| 65 | 5.6 | 2.9 | 3.6 | 1.3 | I. versicolor |
| 66 | 6.7 | 3.1 | 4.4 | 1.4 | I. versicolor |
| 67 | 5.6 | 3.0 | 4.5 | 1.5 | I. versicolor |
| 68 | 5.8 | 2.7 | 4.1 | 1.0 | I. versicolor |
| 69 | 6.2 | 2.2 | 4.5 | 1.5 | I. versicolor |
| 70 | 5.6 | 2.5 | 3.9 | 1.1 | I. versicolor |
| 71 | 5.9 | 3.2 | 4.8 | 1.8 | I. versicolor |
| 72 | 6.1 | 2.8 | 4.0 | 1.3 | I. versicolor |
| 73 | 6.3 | 2.5 | 4.9 | 1.5 | I. versicolor |
| 74 | 6.1 | 2.8 | 4.7 | 1.2 | I. versicolor |
| 75 | 6.4 | 2.9 | 4.3 | 1.3 | I. versicolor |
| 76 | 6.6 | 3.0 | 4.4 | 1.4 | I. versicolor |
| 77 | 6.8 | 2.8 | 4.8 | 1.4 | I. versicolor |
| 78 | 6.7 | 3.0 | 5.0 | 1.7 | I. versicolor |
| 79 | 6.0 | 2.9 | 4.5 | 1.5 | I. versicolor |
| 80 | 5.7 | 2.6 | 3.5 | 1.0 | I. versicolor |
| 81 | 5.5 | 2.4 | 3.8 | 1.1 | I. versicolor |
| 82 | 5.5 | 2.4 | 3.7 | 1.0 | I. versicolor |
| 83 | 5.8 | 2.7 | 3.9 | 1.2 | I. versicolor |
| 84 | 6.0 | 2.7 | 5.1 | 1.6 | I. versicolor |
| 85 | 5.4 | 3.0 | 4.5 | 1.5 | I. versicolor |
| 86 | 6.0 | 3.4 | 4.5 | 1.6 | I. versicolor |
| 87 | 6.7 | 3.1 | 4.7 | 1.5 | I. versicolor |
| 88 | 6.3 | 2.3 | 4.4 | 1.3 | I. versicolor |
| 89 | 5.6 | 3.0 | 4.1 | 1.3 | I. versicolor |
| 90 | 5.5 | 2.5 | 4.0 | 1.3 | I. versicolor |
| 91 | 5.5 | 2.6 | 4.4 | 1.2 | I. versicolor |
| 92 | 6.1 | 3.0 | 4.6 | 1.4 | I. versicolor |
| 93 | 5.8 | 2.6 | 4.0 | 1.2 | I. versicolor |
| 94 | 5.0 | 2.3 | 3.3 | 1.0 | I. versicolor |
| 95 | 5.6 | 2.7 | 4.2 | 1.3 | I. versicolor |
| 96 | 5.7 | 3.0 | 4.2 | 1.2 | I. versicolor |
| 97 | 5.7 | 2.9 | 4.2 | 1.3 | I. versicolor |
| 98 | 6.2 | 2.9 | 4.3 | 1.3 | I. versicolor |
| 99 | 5.1 | 2.5 | 3.0 | 1.1 | I. versicolor |
| 100 | 5.7 | 2.8 | 4.1 | 1.3 | I. versicolor |
| 101 | 6.3 | 3.3 | 6.0 | 2.5 | I. virginica |
| 102 | 5.8 | 2.7 | 5.1 | 1.9 | I. virginica |
| 103 | 7.1 | 3.0 | 5.9 | 2.1 | I. virginica |
| 104 | 6.3 | 2.9 | 5.6 | 1.8 | I. virginica |
| 105 | 6.5 | 3.0 | 5.8 | 2.2 | I. virginica |
| 106 | 7.6 | 3.0 | 6.6 | 2.1 | I. virginica |
| 107 | 4.9 | 2.5 | 4.5 | 1.7 | I. virginica |
| 108 | 7.3 | 2.9 | 6.3 | 1.8 | I. virginica |
| 109 | 6.7 | 2.5 | 5.8 | 1.8 | I. virginica |
| 110 | 7.2 | 3.6 | 6.1 | 2.5 | I. virginica |
| 111 | 6.5 | 3.2 | 5.1 | 2.0 | I. virginica |
| 112 | 6.4 | 2.7 | 5.3 | 1.9 | I. virginica |
| 113 | 6.8 | 3.0 | 5.5 | 2.1 | I. virginica |
| 114 | 5.7 | 2.5 | 5.0 | 2.0 | I. virginica |
| 115 | 5.8 | 2.8 | 5.1 | 2.4 | I. virginica |
| 116 | 6.4 | 3.2 | 5.3 | 2.3 | I. virginica |
| 117 | 6.5 | 3.0 | 5.5 | 1.8 | I. virginica |
| 118 | 7.7 | 3.8 | 6.7 | 2.2 | I. virginica |
| 119 | 7.7 | 2.6 | 6.9 | 2.3 | I. virginica |
| 120 | 6.0 | 2.2 | 5.0 | 1.5 | I. virginica |
| 121 | 6.9 | 3.2 | 5.7 | 2.3 | I. virginica |
| 122 | 5.6 | 2.8 | 4.9 | 2.0 | I. virginica |
| 123 | 7.7 | 2.8 | 6.7 | 2.0 | I. virginica |
| 124 | 6.3 | 2.7 | 4.9 | 1.8 | I. virginica |
| 125 | 6.7 | 3.3 | 5.7 | 2.1 | I. virginica |
| 126 | 7.2 | 3.2 | 6.0 | 1.8 | I. virginica |
| 127 | 6.2 | 2.8 | 4.8 | 1.8 | I. virginica |
| 128 | 6.1 | 3.0 | 4.9 | 1.8 | I. virginica |
| 129 | 6.4 | 2.8 | 5.6 | 2.1 | I. virginica |
| 130 | 7.2 | 3.0 | 5.8 | 1.6 | I. virginica |
| 131 | 7.4 | 2.8 | 6.1 | 1.9 | I. virginica |
| 132 | 7.9 | 3.8 | 6.4 | 2.0 | I. virginica |
| 133 | 6.4 | 2.8 | 5.6 | 2.2 | I. virginica |
| 134 | 6.3 | 2.8 | 5.1 | 1.5 | I. virginica |
| 135 | 6.1 | 2.6 | 5.6 | 1.4 | I. virginica |
| 136 | 7.7 | 3.0 | 6.1 | 2.3 | I. virginica |
| 137 | 6.3 | 3.4 | 5.6 | 2.4 | I. virginica |
| 138 | 6.4 | 3.1 | 5.5 | 1.8 | I. virginica |
| 139 | 6.0 | 3.0 | 4.8 | 1.8 | I. virginica |
| 140 | 6.9 | 3.1 | 5.4 | 2.1 | I. virginica |
| 141 | 6.7 | 3.1 | 5.6 | 2.4 | I. virginica |
| 142 | 6.9 | 3.1 | 5.1 | 2.3 | I. virginica |
| 143 | 5.8 | 2.7 | 5.1 | 1.9 | I. virginica |
| 144 | 6.8 | 3.2 | 5.9 | 2.3 | I. virginica |
| 145 | 6.7 | 3.3 | 5.7 | 2.5 | I. virginica |
| 146 | 6.7 | 3.0 | 5.2 | 2.3 | I. virginica |
| 147 | 6.3 | 2.5 | 5.0 | 1.9 | I. virginica |
| 148 | 6.5 | 3.0 | 5.2 | 2.0 | I. virginica |
| 149 | 6.2 | 3.4 | 5.4 | 2.3 | I. virginica |
| 150 | 5.9 | 3.0 | 5.1 | 1.8 | I. virginica |
The iris data set is widely used as a beginner's dataset for machine learning purposes. The dataset is included in R base and Python in the machine learning library scikit-learn, so that users can access it without having to find a source for it.
Several versions of the dataset have been published.
R code illustrating usage
The example R code shown below reproduce the scatterplot displayed at the top of this article:
# Show the dataset iris # Show the help page, with information about the dataset ?iris # Create scatterplots of all pairwise combination of the 4 variables in the dataset pairs(iris[1:4], main="Iris Data (red=setosa,green=versicolor,blue=virginica)", pch=21, bg=c("red","green3","blue")[unclass(iris$Species)]) Python code illustrating usage
from sklearn.datasets import load_iris iris = load_iris() iris.head() iris.info() This code gives:
{'data': array([[5.1, 3.5, 1.4, 0.2], [4.9, 3., 1.4, 0.2], [4.7, 3.2, 1.3, 0.2], [4.6, 3.1, 1.5, 0.2],... 'target': array([0, 0, 0, ... 1, 1, 1, ... 2, 2, 2, ... 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), ...} See also
- Classic data sets
- List of datasets for machine-learning research
References
- R. A. Fisher (1936). "The use of multiple measurements in taxonomic problems". Annals of Eugenics. 7 (2): 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x. hdl:2440/15227.
- Edgar Anderson (1936). "The species problem in Iris". Annals of the Missouri Botanical Garden. 23 (3): 457–509. doi:10.2307/2394164. JSTOR 2394164.
- Edgar Anderson (1935). "The irises of the Gaspé Peninsula". Bulletin of the American Iris Society. 59: 2–5.
- Gorban, A. N.; Zinovyev, A. (2010). "Principal manifolds and graphs in practice: from molecular biology to dynamical systems". International Journal of Neural Systems. 20 (3): 219–232. arXiv:1001.1122.
- "UCI Machine Learning Repository: Iris Data Set". archive.ics.uci.edu. Retrieved 2017-12-01.
- Ines Färber; Stephan Günnemann; Hans-Peter Kriegel; Peer Kröger; Emmanuel Müller; Erich Schubert; Thomas Seidl; Arthur Zimek (2010). "On Using Class-Labels in Evaluation of Clusterings" (PDF). In Xiaoli Z. Fern; Ian Davidson; Jennifer Dy (eds.). MultiClust: Discovering, Summarizing, and Using Multiple Clusterings. ACM SIGKDD.
- Gorban, A.N.; Sumner, N.R.; Zinovyev, A.Y. (2007). "Topological grammars for data approximation". Applied Mathematics Letters. 20 (4): 382–386.
- Bezdek, J.C.; Keller, J.M.; Krishnapuram, R.; Kuncheva, L.I.; Pal, N.R. (1999). "Will the real iris data please stand up?". IEEE Transactions on Fuzzy Systems. 7 (3): 368–369. doi:10.1109/91.771092.
External links
- "Fisher's Iris Data". (Contains two errors which are documented). UCI Machine Learning Repository: Iris Data Set.
The Iris flower data set or Fisher s Iris data set is a multivariate data set used and made famous by the British statistician 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 Gaspe 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 Scatterplot of the data set The data set consists of 50 samples from each of three species of Iris Iris setosa Iris virginica and Iris versicolor Four features were measured from each sample the length and the width of the sepals and petals in centimeters Based on the combination of these four features Fisher developed a linear discriminant model to distinguish each species Fisher s paper was published in the Annals of Eugenics today the Annals of Human Genetics Use of the data setUnsatisfactory k means clustering the data cannot be clustered into the known classes and actual species visualized using ELKIAn example of the so called metro map for the Iris data set Only a small fraction of Iris virginica is mixed with Iris versicolor All other samples of the different Iris species belong to the different nodes Originally used as an example data set on which Fisher s linear discriminant analysis was applied it became a typical test case for many statistical classification techniques in machine learning such as support vector machines The use of this data set in cluster analysis however is not common since the data set only contains two clusters with rather obvious separation One of the clusters contains Iris setosa while the other cluster contains both Iris virginica and Iris versicolor and is not separable without the species information Fisher used This makes the data set a good example to explain the difference between supervised and unsupervised techniques in data mining Fisher s linear discriminant model can only be obtained when the object species are known class labels and clusters are not necessarily the same Nevertheless all three species of Iris are separable in the projection on the nonlinear and branching principal component The data set is approximated by the closest tree with some penalty for the excessive number of nodes bending and stretching Then the so called metro map is constructed The data points are projected into the closest node For each node the pie diagram of the projected points is prepared The area of the pie is proportional to the number of the projected points It is clear from the diagram left that the absolute majority of the samples of the different Iris species belong to the different nodes Only a small fraction of Iris virginica is mixed with Iris versicolor the mixed blue green nodes in the diagram Therefore the three species of Iris Iris setosa Iris virginica and Iris versicolor are separable by the unsupervising procedures of nonlinear principal component analysis To discriminate them it is sufficient just to select the corresponding nodes on the principal tree Data setIris setosa The dataset contains a set of 150 records under five attributes sepal length sepal width petal length petal width and species Iris versicolorIris virginicaSpectramap biplot of Fisher s iris data setFisher s Iris data Dataset order Sepal length Sepal width Petal length Petal width Species1 5 1 3 5 1 4 0 2 I setosa2 4 9 3 0 1 4 0 2 I setosa3 4 7 3 2 1 3 0 2 I setosa4 4 6 3 1 1 5 0 2 I setosa5 5 0 3 6 1 4 0 3 I setosa6 5 4 3 9 1 7 0 4 I setosa7 4 6 3 4 1 4 0 3 I setosa8 5 0 3 4 1 5 0 2 I setosa9 4 4 2 9 1 4 0 2 I setosa10 4 9 3 1 1 5 0 1 I setosa11 5 4 3 7 1 5 0 2 I setosa12 4 8 3 4 1 6 0 2 I setosa13 4 8 3 0 1 4 0 1 I setosa14 4 3 3 0 1 1 0 1 I setosa15 5 8 4 0 1 2 0 2 I setosa16 5 7 4 4 1 5 0 4 I setosa17 5 4 3 9 1 3 0 4 I setosa18 5 1 3 5 1 4 0 3 I setosa19 5 7 3 8 1 7 0 3 I setosa20 5 1 3 8 1 5 0 3 I setosa21 5 4 3 4 1 7 0 2 I setosa22 5 1 3 7 1 5 0 4 I setosa23 4 6 3 6 1 0 0 2 I setosa24 5 1 3 3 1 7 0 5 I setosa25 4 8 3 4 1 9 0 2 I setosa26 5 0 3 0 1 6 0 2 I setosa27 5 0 3 4 1 6 0 4 I setosa28 5 2 3 5 1 5 0 2 I setosa29 5 2 3 4 1 4 0 2 I setosa30 4 7 3 2 1 6 0 2 I setosa31 4 8 3 1 1 6 0 2 I setosa32 5 4 3 4 1 5 0 4 I setosa33 5 2 4 1 1 5 0 1 I setosa34 5 5 4 2 1 4 0 2 I setosa35 4 9 3 1 1 5 0 2 I setosa36 5 0 3 2 1 2 0 2 I setosa37 5 5 3 5 1 3 0 2 I setosa38 4 9 3 6 1 4 0 1 I setosa39 4 4 3 0 1 3 0 2 I setosa40 5 1 3 4 1 5 0 2 I setosa41 5 0 3 5 1 3 0 3 I setosa42 4 5 2 3 1 3 0 3 I setosa43 4 4 3 2 1 3 0 2 I setosa44 5 0 3 5 1 6 0 6 I setosa45 5 1 3 8 1 9 0 4 I setosa46 4 8 3 0 1 4 0 3 I setosa47 5 1 3 8 1 6 0 2 I setosa48 4 6 3 2 1 4 0 2 I setosa49 5 3 3 7 1 5 0 2 I setosa50 5 0 3 3 1 4 0 2 I setosa51 7 0 3 2 4 7 1 4 I versicolor52 6 4 3 2 4 5 1 5 I versicolor53 6 9 3 1 4 9 1 5 I versicolor54 5 5 2 3 4 0 1 3 I versicolor55 6 5 2 8 4 6 1 5 I versicolor56 5 7 2 8 4 5 1 3 I versicolor57 6 3 3 3 4 7 1 6 I versicolor58 4 9 2 4 3 3 1 0 I versicolor59 6 6 2 9 4 6 1 3 I versicolor60 5 2 2 7 3 9 1 4 I versicolor61 5 0 2 0 3 5 1 0 I versicolor62 5 9 3 0 4 2 1 5 I versicolor63 6 0 2 2 4 0 1 0 I versicolor64 6 1 2 9 4 7 1 4 I versicolor65 5 6 2 9 3 6 1 3 I versicolor66 6 7 3 1 4 4 1 4 I versicolor67 5 6 3 0 4 5 1 5 I versicolor68 5 8 2 7 4 1 1 0 I versicolor69 6 2 2 2 4 5 1 5 I versicolor70 5 6 2 5 3 9 1 1 I versicolor71 5 9 3 2 4 8 1 8 I versicolor72 6 1 2 8 4 0 1 3 I versicolor73 6 3 2 5 4 9 1 5 I versicolor74 6 1 2 8 4 7 1 2 I versicolor75 6 4 2 9 4 3 1 3 I versicolor76 6 6 3 0 4 4 1 4 I versicolor77 6 8 2 8 4 8 1 4 I versicolor78 6 7 3 0 5 0 1 7 I versicolor79 6 0 2 9 4 5 1 5 I versicolor80 5 7 2 6 3 5 1 0 I versicolor81 5 5 2 4 3 8 1 1 I versicolor82 5 5 2 4 3 7 1 0 I versicolor83 5 8 2 7 3 9 1 2 I versicolor84 6 0 2 7 5 1 1 6 I versicolor85 5 4 3 0 4 5 1 5 I versicolor86 6 0 3 4 4 5 1 6 I versicolor87 6 7 3 1 4 7 1 5 I versicolor88 6 3 2 3 4 4 1 3 I versicolor89 5 6 3 0 4 1 1 3 I versicolor90 5 5 2 5 4 0 1 3 I versicolor91 5 5 2 6 4 4 1 2 I versicolor92 6 1 3 0 4 6 1 4 I versicolor93 5 8 2 6 4 0 1 2 I versicolor94 5 0 2 3 3 3 1 0 I versicolor95 5 6 2 7 4 2 1 3 I versicolor96 5 7 3 0 4 2 1 2 I versicolor97 5 7 2 9 4 2 1 3 I versicolor98 6 2 2 9 4 3 1 3 I versicolor99 5 1 2 5 3 0 1 1 I versicolor100 5 7 2 8 4 1 1 3 I versicolor101 6 3 3 3 6 0 2 5 I virginica102 5 8 2 7 5 1 1 9 I virginica103 7 1 3 0 5 9 2 1 I virginica104 6 3 2 9 5 6 1 8 I virginica105 6 5 3 0 5 8 2 2 I virginica106 7 6 3 0 6 6 2 1 I virginica107 4 9 2 5 4 5 1 7 I virginica108 7 3 2 9 6 3 1 8 I virginica109 6 7 2 5 5 8 1 8 I virginica110 7 2 3 6 6 1 2 5 I virginica111 6 5 3 2 5 1 2 0 I virginica112 6 4 2 7 5 3 1 9 I virginica113 6 8 3 0 5 5 2 1 I virginica114 5 7 2 5 5 0 2 0 I virginica115 5 8 2 8 5 1 2 4 I virginica116 6 4 3 2 5 3 2 3 I virginica117 6 5 3 0 5 5 1 8 I virginica118 7 7 3 8 6 7 2 2 I virginica119 7 7 2 6 6 9 2 3 I virginica120 6 0 2 2 5 0 1 5 I virginica121 6 9 3 2 5 7 2 3 I virginica122 5 6 2 8 4 9 2 0 I virginica123 7 7 2 8 6 7 2 0 I virginica124 6 3 2 7 4 9 1 8 I virginica125 6 7 3 3 5 7 2 1 I virginica126 7 2 3 2 6 0 1 8 I virginica127 6 2 2 8 4 8 1 8 I virginica128 6 1 3 0 4 9 1 8 I virginica129 6 4 2 8 5 6 2 1 I virginica130 7 2 3 0 5 8 1 6 I virginica131 7 4 2 8 6 1 1 9 I virginica132 7 9 3 8 6 4 2 0 I virginica133 6 4 2 8 5 6 2 2 I virginica134 6 3 2 8 5 1 1 5 I virginica135 6 1 2 6 5 6 1 4 I virginica136 7 7 3 0 6 1 2 3 I virginica137 6 3 3 4 5 6 2 4 I virginica138 6 4 3 1 5 5 1 8 I virginica139 6 0 3 0 4 8 1 8 I virginica140 6 9 3 1 5 4 2 1 I virginica141 6 7 3 1 5 6 2 4 I virginica142 6 9 3 1 5 1 2 3 I virginica143 5 8 2 7 5 1 1 9 I virginica144 6 8 3 2 5 9 2 3 I virginica145 6 7 3 3 5 7 2 5 I virginica146 6 7 3 0 5 2 2 3 I virginica147 6 3 2 5 5 0 1 9 I virginica148 6 5 3 0 5 2 2 0 I virginica149 6 2 3 4 5 4 2 3 I virginica150 5 9 3 0 5 1 1 8 I virginica The iris data set is widely used as a beginner s dataset for machine learning purposes The dataset is included in R base and Python in the machine learning library scikit learn so that users can access it without having to find a source for it Several versions of the dataset have been published R code illustrating usage The example R code shown below reproduce the scatterplot displayed at the top of this article Show the dataset iris Show the help page with information about the dataset iris Create scatterplots of all pairwise combination of the 4 variables in the dataset pairs iris 1 4 main Iris Data red setosa green versicolor blue virginica pch 21 bg c red green3 blue unclass iris Species Python code illustrating usage from sklearn datasets import load iris iris load iris iris head iris info This code gives data array 5 1 3 5 1 4 0 2 4 9 3 1 4 0 2 4 7 3 2 1 3 0 2 4 6 3 1 1 5 0 2 target array 0 0 0 1 1 1 2 2 2 target names array setosa versicolor virginica dtype lt U10 See alsoClassic data sets List of datasets for machine learning researchReferencesR A Fisher 1936 The use of multiple measurements in taxonomic problems Annals of Eugenics 7 2 179 188 doi 10 1111 j 1469 1809 1936 tb02137 x hdl 2440 15227 Edgar Anderson 1936 The species problem in Iris Annals of the Missouri Botanical Garden 23 3 457 509 doi 10 2307 2394164 JSTOR 2394164 Edgar Anderson 1935 The irises of the Gaspe Peninsula Bulletin of the American Iris Society 59 2 5 Gorban A N Zinovyev A 2010 Principal manifolds and graphs in practice from molecular biology to dynamical systems International Journal of Neural Systems 20 3 219 232 arXiv 1001 1122 UCI Machine Learning Repository Iris Data Set archive ics uci edu Retrieved 2017 12 01 Ines Farber Stephan Gunnemann Hans Peter Kriegel Peer Kroger Emmanuel Muller Erich Schubert Thomas Seidl Arthur Zimek 2010 On Using Class Labels in Evaluation of Clusterings PDF In Xiaoli Z Fern Ian Davidson Jennifer Dy eds MultiClust Discovering Summarizing and Using Multiple Clusterings ACM SIGKDD Gorban A N Sumner N R Zinovyev A Y 2007 Topological grammars for data approximation Applied Mathematics Letters 20 4 382 386 Bezdek J C Keller J M Krishnapuram R Kuncheva L I Pal N R 1999 Will the real iris data please stand up IEEE Transactions on Fuzzy Systems 7 3 368 369 doi 10 1109 91 771092 External links Fisher s Iris Data Contains two errors which are documented UCI Machine Learning Repository Iris Data Set
