sklearn.datasets.load_iris

sklearn.datasets.load_iris()[source]

Load and return the iris dataset (classification).

The iris dataset is a classic and very easy multi-class classification dataset.

Classes 3
Samples per class 50
Samples total 150
Dimensionality 4
Features real, positive
Returns:

data : Bunch

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

Examples

Let’s say you are interested in the samples 10, 25, and 50, and want to know their class name.

>>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']