sklearn.datasets.load_digits

sklearn.datasets.load_digits(n_class=10)

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

Parameters:

n_class : integer, between 0 and 10, optional (default=10)

The number of classes to return.

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.

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 pylab as pl 
>>> pl.gray() 
>>> pl.matshow(digits.images[0]) 
>>> pl.show() 

Examples using sklearn.datasets.load_digits

Pipelining: chaining a PCA and a logistic regression

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

The Digit Dataset

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

Restricted Boltzmann Machine features for digit classification

Label Propagation digits: Demonstrating performance

Label Propagation digits active learning

SVM-Anova: SVM with univariate feature selection