sklearn.datasets
.load_digits¶
-
sklearn.datasets.
load_digits
(n_class=10, return_X_y=False)[source]¶ Load and return the digits dataset (classification).
Each datapoint is a 8x8 image of a digit.
Classes 10 Samples per class ~180 Samples total 1797 Dimensionality 64 Features integers 0-16 Read more in the User Guide.
Parameters: n_class : integer, between 0 and 10, optional (default=10)
The number of classes to return.
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, ‘images’, the images corresponding to each sample, ‘target’, the classification labels for each sample, ‘target_names’, the meaning of the labels, and ‘DESCR’, the full description of the dataset.
(data, target) : tuple if
return_X_y
is TrueNew in version 0.18.
This is a copy of the test set of the UCI ML hand-written digits datasets :
http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits :
Examples
To load the data and visualize the images:
>>> from sklearn.datasets import load_digits >>> digits = load_digits() >>> print(digits.data.shape) (1797, 64) >>> import matplotlib.pyplot as plt >>> plt.gray() >>> plt.matshow(digits.images[0]) >>> plt.show()