iris.names

machine learning, accuracy estimation, cross-validation, bootstrap, ID3, decision trees, decision

| Machine readable .names file for MLC++ library.
| Commented UC Irvine .names follows with machine readable info at end of file.
| 
| ------
| 
| 1. Title: Iris Plants Database
| 
| 2. Sources:
|      (a) Creator: R.A. Fisher
|      (b) Donor: Michael Marshall ([e-mail])
|      (c) Date: July, 1988
| 
| 3. Past Usage:
|    - Publications: too many to mention!!!  Here are a few.
|    1. 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).
|    2. 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.
|    3. 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)
|    4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE 
|       Transactions on Information Theory, May 1972, 431-433.
|       -- Results:
|          -- very low misclassification rates again
|    5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
|       conceptual clustering system finds 3 classes in the data.
| 
| 4. 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.
| 
| 5. Number of Instances: 150 (50 in each of three classes)
| 
| 6. Number of Attributes: 4 numeric, predictive attributes and the class
| 
| 7. Attribute Information:
|    1. sepal length in cm
|    2. sepal width in cm
|    3. petal length in cm
|    4. petal width in cm
|    5. class: 
|       -- Iris Setosa
|       -- Iris Versicolour
|       -- Iris Virginica
| 
| 8. 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!)
| 
| 9. Class Distribution: 33.3% for each of 3 classes.
|
| -----

Iris-setosa, Iris-versicolor,
Iris-virginica			| classes

sepal-length:			continuous.
sepal-width:			continuous.
petal-length:			continuous.
petal-width:			continuous.