sklearn.datasets.load_digits

sklearn.datasets.load_digits(n_class=10, return_X_y=False)

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 True

New 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() 

Examples using sklearn.datasets.load_digits

The Johnson-Lindenstrauss bound for embedding with random projections

Explicit feature map approximation for RBF kernels

Recognizing hand-written digits

Feature agglomeration

Various Agglomerative Clustering on a 2D embedding of digits

A demo of K-Means clustering on the handwritten digits data

Pipelining: chaining a PCA and a logistic regression

Selecting dimensionality reduction with Pipeline and GridSearchCV

The Digit Dataset

Early stopping of Gradient Boosting

Digits Classification Exercise

Cross-validation on Digits Dataset Exercise

Recursive feature elimination

Comparing various online solvers

L1 Penalty and Sparsity in Logistic Regression

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…

Plotting Validation Curves

Parameter estimation using grid search with cross-validation

Comparing randomized search and grid search for hyperparameter estimation

Plotting Learning Curves

Kernel Density Estimation

Compare Stochastic learning strategies for MLPClassifier

Restricted Boltzmann Machine features for digit classification

Label Propagation digits: Demonstrating performance

Label Propagation digits active learning