sklearn.datasets
.load_iris¶
-
sklearn.datasets.
load_iris
(return_X_y=False)[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 Read more in the User Guide.
Parameters: return_X_y : boolean, default=False.
If True, returns
(data, target)
instead of a Bunch object. See below for more information about the data and target object.New in version 0.18.
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.
(data, target) : tuple if
return_X_y
is TrueNew in version 0.18.
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']