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sklearn.datasets.load_iris(return_X_y=False)
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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 thedata
andtarget
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']
sklearn.datasets.load_iris()
Examples using
2017-01-15 04:25:47
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