sklearn.datasets.load_wine

sklearn.datasets.load_wine(return_X_y=False)

Load and return the wine dataset (classification).

New in version 0.18.

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

Classes	3
Samples per class	[59,71,48]
Samples total	178
Dimensionality	13
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.

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 True

The copy of UCI ML Wine Data Set dataset is downloaded and modified to fit :

standard format from: :

https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data :

Examples

Let’s say you are interested in the samples 10, 80, and 140, and want to know their class name.

>>> from sklearn.datasets import load_wine
>>> data = load_wine()
>>> data.target[[10, 80, 140]]
array([0, 1, 2])
>>> list(data.target_names)
['class_0', 'class_1', 'class_2']

Examples using sklearn.datasets.load_wine

Importance of Feature Scaling