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

https://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

Balance model complexity and cross-validated score

Plotting Learning Curves

Kernel Density Estimation

Dimensionality Reduction with Neighborhood Components Analysis

Restricted Boltzmann Machine features for digit classification

Compare Stochastic learning strategies for MLPClassifier

Label Propagation digits: Demonstrating performance

Label Propagation digits active learning