Iris

Donated on 6/30/1988

A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods.

Dataset Characteristics

Tabular

Subject Area

Biology

Associated Tasks

Classification

Feature Type

Real

# Instances

150

# Features

4

Dataset Information

What do the instances in this dataset represent?

Each instance is a plant

Additional Information

This is one of the earliest datasets used in the literature on classification methods and widely used in statistics and machine learning. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are not linearly separable from each other. Predicted attribute: class of iris plant. This is an exceedingly simple domain. This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick@espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.

Has Missing Values?

No

Introductory Paper

The Iris data set: In search of the source of virginica

By A. Unwin, K. Kleinman. 2021

Published in Significance, 2021

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
sepal lengthFeatureContinuouscmno
sepal widthFeatureContinuouscmno
petal lengthFeatureContinuouscmno
petal widthFeatureContinuouscmno
classTargetCategoricalclass of iris plant: Iris Setosa, Iris Versicolour, or Iris Virginicano

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Baseline Model Performance

Dataset Files

FileSize
bezdekIris.data4.4 KB
iris.data4.4 KB
iris.names2.9 KB
Index105 Bytes

Papers Citing this Dataset

Convergence and Margin of Adversarial Training on Separable Data

By Zachary Charles, Shashank Rajput, Stephen Wright, Dimitris Papailiopoulos. 2019

Published in ArXiv.

CRAD: Clustering with Robust Autocuts and Depth

By Xin Huang, Yulia Gel. 2019

Published in 2017 IEEE International Conference on Data Mining (ICDM), 925--930} (2017).

Deep Spiking Neural Network with Spike Count based Learning Rule

By Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li. 2019

Published in ArXiv.

Bounded Fuzzy Possibilistic Method

By Hossein Yazdani. 2019

Published in ArXiv.

0 to 5 of 352

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352 citations
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Keywords

Creators

R. A. Fisher

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