iris#
The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
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.
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.
And many more..
The typical ML task in this dataset is to build a model that classifies the type of flower.
- Dataset Shape:
Dataset Shape# Property
Value
Samples Total
150
Dimensionality
4
Features
real
Targets
3
Samples per class
50
- Description:
Dataset Description# Column name
Column Role
Description
sepal length (cm)
Feature
The length of the flower’s sepal (in cm)
sepal width (cm)
Feature
The width of the flower’s sepal (in cm)
petal length (cm)
Feature
The length of the flower’s petal (in cm)
petal width (cm)
Feature
The width of the flower’s petal (in cm)
target
Label
The class (Setosa,Versicolour,Virginica)
Functions
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Load and returns the Iris dataset (classification). |
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Load and return a fitted classification model to predict the flower type in the iris dataset. |