sklearn.datasets.make_sparse_uncorrelated¶
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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_samples : int, optional (default=100)
The number of samples.
- n_features : int, optional (default=10)
The number of features.
- random_state : int, RandomState instance or None (default)
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
Returns: - X : array of shape [n_samples, n_features]
The input samples.
- y : array 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.