63 lines
2.7 KiB
ReStructuredText
63 lines
2.7 KiB
ReStructuredText
|
.. _iris_dataset:
|
||
|
|
|
||
|
|
Iris plants dataset
|
||
|
|
--------------------
|
||
|
|
|
||
|
|
**Data Set Characteristics:**
|
||
|
|
|
||
|
|
:Number of Instances: 150 (50 in each of three classes)
|
||
|
|
:Number of Attributes: 4 numeric, predictive attributes and the class
|
||
|
|
:Attribute Information:
|
||
|
|
- sepal length in cm
|
||
|
|
- sepal width in cm
|
||
|
|
- petal length in cm
|
||
|
|
- petal width in cm
|
||
|
|
- class:
|
||
|
|
- Iris-Setosa
|
||
|
|
- Iris-Versicolour
|
||
|
|
- Iris-Virginica
|
||
|
|
|
||
|
|
: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!)
|
||
|
|
============== ==== ==== ======= ===== ====================
|
||
|
|
|
||
|
|
:Missing Attribute Values: None
|
||
|
|
:Class Distribution: 33.3% for each of 3 classes.
|
||
|
|
:Creator: R.A. Fisher
|
||
|
|
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
|
||
|
|
:Date: July, 1988
|
||
|
|
|
||
|
|
The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
|
||
|
|
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
|
||
|
|
Machine Learning Repository, which has two wrong data points.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
.. topic:: References
|
||
|
|
|
||
|
|
- 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).
|
||
|
|
- 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.
|
||
|
|
- 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.
|
||
|
|
- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
|
||
|
|
on Information Theory, May 1972, 431-433.
|
||
|
|
- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
|
||
|
|
conceptual clustering system finds 3 classes in the data.
|
||
|
|
- Many, many more ...
|