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_classinteger, between 0 and 10, optional (default=10)

The number of classes to return.

return_X_yboolean, 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

dataBunch

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¶

The Digit Dataset¶

Early stopping of Gradient Boosting¶

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¶

Caching nearest neighbors¶

Dimensionality Reduction with Neighborhood Components Analysis¶

Restricted Boltzmann Machine features for digit classification¶

Compare Stochastic learning strategies for MLPClassifier¶

Pipelining: chaining a PCA and a logistic regression¶

Selecting dimensionality reduction with Pipeline and GridSearchCV¶

Label Propagation digits: Demonstrating performance¶

Label Propagation digits active learning¶

Digits Classification Exercise¶

Cross-validation on Digits Dataset Exercise¶