sklearn.datasets.make_sparse_uncorrelated(n_samples=100, n_features=10, *, random_state=None)[source]#

Generate a random regression problem with sparse uncorrelated design.

This dataset is described in Celeux et al [1]. as:

X ~ N(0, 1)
y(X) = X[:, 0] + 2 * X[:, 1] - 2 * X[:, 2] - 1.5 * X[:, 3]

Only the first 4 features are informative. The remaining features are useless.

Read more in the User Guide.

n_samplesint, default=100

The number of samples.

n_featuresint, default=10

The number of features.

random_stateint, RandomState instance or None, default=None

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

Xndarray of shape (n_samples, n_features)

The input samples.

yndarray of shape (n_samples,)

The output values.



G. Celeux, M. El Anbari, J.-M. Marin, C. P. Robert, “Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation”, 2009.


>>> from sklearn.datasets import make_sparse_uncorrelated
>>> X, y = make_sparse_uncorrelated(random_state=0)
>>> X.shape
(100, 10)
>>> y.shape