scikits.learn.datasets.load_iris

scikits.learn.datasets.load_iris()

load the iris dataset and returns it.

Returns :

data : Bunch

Dictionnary-like object, the interesting attributes are: ‘data’, the data to learn, ‘target’, the classification labels, ‘target_names’, the meaning of the labels, and ‘DESCR’, the full description of the dataset.

Notes

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:  

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

This is a copy of UCI ML iris datasets. http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

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.