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.
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Changed in version 0.20: Fixed two wrong data points according to Fisher’s paper. The new version is the same as in R, but not as in the UCI Machine Learning Repository.
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']
sklearn.datasets.load_iris
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html