sklearn.datasets.make_sparse_uncorrelated¶
- 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.
- Parameters
- 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.
- Returns
- Xndarray of shape (n_samples, n_features)
The input samples.
- yndarray of shape (n_samples,)
The output values.
References
- 1
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.