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

load_data([data_format, as_train_test])

Load and returns the Iris dataset (classification).

load_fitted_model([pretrained])

Load and return a fitted classification model to predict the flower type in the iris dataset.