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# `sklearn.datasets`.make_sparse_uncorrelated

sklearn.datasets.make_sparse_uncorrelated(n_samples=100, n_features=10, *, random_state=None)

  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, default=100

      The number of samples.

    n_features int, default=10

      The number of features.

    random_state int, 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:
    X ndarray of shape (n_samples, n_features)

      The input samples.

    y ndarray 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.