Summary

(No information yet)

License

unknown (from Weka repository)

Dependencies

Tags

arff slurped Weka

Attribute Types

Floating Point,String

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# Instances: 150 / # Attributes: 5
HDF5 (24.4 KB) XML CSV ARFF LibSVM Matlab Octave

Original Data Format

arff

Name

iris

Version mldata

0

Comment

1. Title: Iris Plants Database

2. Sources: (a) Creator: R.A. Fisher (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) (c) Date: July, 1988

3. Past Usage:

4. Publications: too many to mention!!! Here are a few.

5. Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950).

6. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

7. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71. -- Results: -- very low misclassification rates (0% for the setosa class)

8. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433. -- Results: -- very low misclassification rates again

9. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II conceptual clustering system finds 3 classes in the data.

10. Relevant Information: --- This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) 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.

11. Number of Instances: 150 (50 in each of three classes)

12. Number of Attributes: 4 numeric, predictive attributes and the class

13. Attribute Information:

14. sepal length in cm

15. sepal width in cm

16. petal length in cm

17. petal width in cm

18. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

19. Missing Attribute Values: None

Summary Statistics: Min Max Mean SD Class Correlation sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)

1. Class Distribution: 33.3% for each of 3 classes.

Names

sepallength,sepalwidth,petallength,petalwidth,class,

Types

1. numeric
2. numeric
3. numeric
4. numeric
5. nominal:Iris-setosa,Iris-versicolor,Iris-virginica

Data (first 10 data points)

sepa...	sepa...	peta...	peta...	class
5.1	3.5	1.4	0.2	Iris...
4.9	3.0	1.4	0.2	Iris...
4.7	3.2	1.3	0.2	Iris...
4.6	3.1	1.5	0.2	Iris...
5.0	3.6	1.4	0.2	Iris...
5.4	3.9	1.7	0.4	Iris...
4.6	3.4	1.4	0.3	Iris...
5.0	3.4	1.5	0.2	Iris...
4.4	2.9	1.4	0.2	Iris...
4.9	3.1	1.5	0.1	Iris...
...	...	...	...	...

Description

A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).

URLs

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Publications

Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html

Measurement Details

Usage Scenario

revision 1

by mldata on 2010-04-29 20:06

revision 2

by phoyer on 2010-08-31 09:12

revision 3

by phoyer on 2010-08-31 09:25

revision 4

by phoyer on 2010-08-31 07:27

revision 5

by phoyer on 2010-08-31 07:27

revision 6

by phoyer on 2010-08-31 09:30

revision 7

by phoyer on 2011-09-14 15:17

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